Utilizing machine learning to perform a merger and optimization operation

ABSTRACT

A device may comprise a memory and a processor coupled to the memory. The processor may receive transaction information and entity information for a plurality of entities and may generate a first model based on the transaction information, the entity information, and information identifying an event, a theme, or a transaction parameter. The processor may process, using the first model, the transaction information and the entity information to identify a set of related entities and a type of relationship associated with the set of related entities. The processor may determine, using a second model, one or more modifications to a first set of accounts and a second set of accounts associated with the first and second entities based on the type of relationship and may perform one or more actions based on the one or more modifications.

RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/814,624, filed Mar. 10, 2020 (now U.S. Pat. No. 11,216,730), which isincorporated herein by reference in its entirety.

BACKGROUND

Technologies for processing transaction-related data enable data to beprocessed to provide information to computing components for intelligentanalysis and feedback to users. Monitoring transactions enables back-endand client-side systems to analyze and provide targeted feedback.

SUMMARY

According to some implementations, a method may include receiving, by adevice, transaction information for a plurality of entities, wherein thetransaction information identifies a plurality of transactionsassociated with the plurality of entities; receiving, by the device,entity information associated with the plurality of entities;performing, by the device, a training operation when generating a firstmodel by portioning the transaction information, the entity information,and information identifying an event, a theme, or a transactionparameter associated with a plurality of types of relationships into atraining set, a validation set, and a test set, wherein performing thetraining operation comprises: using the training set to fit the firstmodel, using the validation set to provide an evaluation of a fit of thefirst model on the training set while tuning the first model, and usingthe test set to provide an evaluation of the first model on the trainingset; processing, by the device and using the first model, thetransaction information and the entity information to identify a set ofrelated entities and a type of relationship associated with the set ofrelated entities, wherein the set of related entities is a subset of theplurality of entities, wherein the set of related entities includes afirst entity and a second entity, wherein the first model receives, asinputs, the transaction information and the entity information, andwherein the first model outputs information identifying the set ofrelated entities and the type of relationship associated with the set ofrelated entities based on the set of related entities being associatedwith the event, the theme, or the transaction parameter; determining, bythe device and using a second model, one or more first modifications toa first set of accounts associated with the first entity and one or moresecond modifications to a second set of accounts associated with thesecond entity based on the type of relationship associated with the setof related entities, wherein the second model receives informationidentifying the first set of accounts, information identifying thesecond set of accounts, and information identifying the type ofrelationship, and wherein the second model outputs informationidentifying the one or more first modifications and the one or moresecond modifications; and performing, by the device, one or more actionsbased on at least one of the determined one or more first modificationsor the determined one or more second modifications.

According to some implementations, a device may include one or morememories; and one or more processors, communicatively coupled to the oneor more memories, configured to: receive transaction information for aplurality of entities, wherein the transaction information identifies aplurality of transactions associated with the plurality of entities;receive entity information associated with the plurality of entities;generate a first model based on the transaction information, the entityinformation, and information identifying an event, a theme, or atransaction parameter associated with a plurality of types ofrelationships; process, using the first model, the transactioninformation and the entity information to identify a set of relatedentities and a type of relationship associated with the set of relatedentities, wherein the set of related entities is a subset of theplurality of entities, wherein the set of related entities includes afirst entity and a second entity, wherein the first model receives, asinputs, the transaction information and the entity information, andwherein the first model outputs information identifying the set ofrelated entities and the type of relationship associated with the set ofrelated entities based on the set of related entities being associatedwith the event, the theme, or the transaction parameter; determine,using a second model, one or more first modifications to a first set ofaccounts associated with the first entity and one or more secondmodifications to a second set of accounts associated with the secondentity based on the type of relationship associated with the set ofrelated entities, wherein the second model receives informationidentifying the first set of accounts, information identifying thesecond set of accounts, and information identifying the type ofrelationship, and wherein the second model outputs informationidentifying the one or more first modifications and the one or moresecond modifications; and perform one or more actions based on at leastone of the one or more first modifications or the one or more secondmodifications.

According to some implementations, a non-transitory computer-readablemedium may store one or more instructions. The one or more instructions,when executed by one or more processors of a device, may cause the oneor more processors to: receive transaction information for a pluralityof entities, wherein the transaction information identifies a pluralityof transactions associated with the plurality of entities; receiveentity information associated with the plurality of entities; process,using a first model, the transaction information and the entityinformation to identify a set of related entities and a type ofrelationship associated with the set of related entities, wherein theset of related entities is a subset of the plurality of entities,wherein the set of related entities includes a first entity and a secondentity, wherein the first model receives, as inputs, the transactioninformation and the entity information, and wherein the first modeloutputs information identifying the set of related entities and the typeof relationship associated with the set of related entities based on theset of related entities being associated with an event, a theme, or atransaction parameter; determine, using a second model, one or morepotential modifications to a set of accounts associated with the set ofrelated entities based on the type of relationship associated with theset of related entities, wherein the second model receives informationidentifying the set of accounts and information identifying the type ofrelationship, and wherein the second model outputs informationidentifying the one or more potential modifications; and provide, via anetwork, information identifying the one or more potentialmodifications.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1L are diagrams of one or more example implementationsdescribed herein.

FIG. 2 is a diagram illustrating an example of training a machinelearning model.

FIG. 3 is a diagram illustrating an example of applying a trainedmachine learning model to a new observation.

FIG. 4 is a diagram illustrating an example of training a machinelearning model.

FIG. 5 is a diagram illustrating an example of applying a trainedmachine learning model to a new observation.

FIG. 6 is a diagram illustrating an example of training a machinelearning model.

FIG. 7 is a diagram illustrating an example of applying a trainedmachine learning model to a new observation.

FIG. 8 is a diagram of an example environment in which systems and/ormethods described herein may be implemented.

FIG. 9 is a diagram of example components of one or more devices of FIG.8.

FIG. 10 is a flowchart of an example process for performing a merger andoptimization operation.

DETAILED DESCRIPTION

The following detailed description of example implementations refers tothe accompanying drawings. The same reference numbers in differentdrawings may identify the same or similar elements.

A person (sometimes referred to herein as an entity) may have a numberof accounts of varying types. For example, a person may have one or morefinancial accounts (e.g., a personal banking account (e.g., a checkingaccount, a savings account, and/or another type of personal bankingaccount), an investment account (e.g., a retirement account, a brokerageaccount, a 529 account, and/or another type of investment account),and/or another type of financial account), one or more informationalaccounts (e.g., an account for accessing a web site of, or receivinginformational articles from, an information source (e.g., a newspaper, anews channel, a weather channel, a blog, and/or another type ofinformation source) and/or another type of informational account), oneor more entertainment accounts (e.g., an account for viewing streamingmedia, an account for accessing electronic books, an account foraccessing audio books, and/or another type of entertainment account),one or more social media accounts (e.g., a Facebook account, a Twitteraccount, a LinkedIn account, and/or another type of social mediaaccount), and/or one or more other types of accounts.

A set of related entities refers to a group of entities (e.g., people,businesses, and/or the like) that have entered into a type ofrelationship (e.g., a marriage, a legal union, a parent-childrelationship, a partnership, a financial and/or business relationship,and/or another type of relationship).

When a person enters into a relationship with another person and becomesa set of related entities, each person may have numerous accounts ofvarious types. It may be difficult for the people to determine whichaccounts to modify to form a joint account, whether to open a newaccount, whether to close an account, etc. As a result, accounts aresometimes poorly maintained when a group of people enter into arelationship.

The poor maintenance of accounts may result in account providersutilizing valuable computing resources (e.g., processor resources,memory resources, and/or the like) to maintain accounts that entitieshave stopped using, but have not closed. Account providers may utilizecomputer resources to determine suggested modifications to the accountsof the related entities. However, account providers may not be aware ofwhat types of accounts the related entities have, a current financialsituation of the related entities, and/or other information that may beuseful for recommending modifications to the accounts of the relatedentities. As a result, the account providers may provide recommendationsthat are declined or ignored by the related entities thereby resultingin the waste of computing resources utilized to determine and/or providethe recommendations.

Some implementations described herein may train a first model using adataset of transactions (e.g., financial transaction information,consumer transaction information, etc.), entity information associatedwith the transactions, and events, themes, or transaction parametersrelating to the transactions. The first model may identify sets ofrelated entities and types of relationships (e.g., a marriagerelationship, a parent-child relationship, a business relationship,etc.) associated with the sets of related entities.

Some implementations described herein may train a second model using adata set of sets of related entities, types of relationships associatedwith the sets of related entities, and account information associatedwith the sets of related entities. The second model may identifymodifications to a set of accounts associated with a set of relatedentities associated with a particular type of relationship. Someimplementations described herein may perform a modification to the setof accounts associated with the set of related entities, may provideinformation identifying the modification to the set of accounts to oneor more entities included in the set of entities, and/or the like.

In this way, an automated process may identify modifications to a set ofaccounts associated with a set of related entities using entityinformation and transaction information associated with an entity. Theautomated process may remove human subjectivity and waste from theprocess, which may improve speed and efficiency of the process andconserve computing resources (e.g., processor resources, memoryresources, and/or the like). Furthermore, some implementations describedherein use a rigorous, computerized process to perform tasks oractivities that were not previously performed. For example, previously,there did not exist a technique to identify a set of related entities, atype of relationship associated with the set of related entities, and/orto identify modifications to a set of accounts associated with the setof related entities based on transaction information and entityinformation associated with an entity that performs the transactions.Accordingly, computing resources associated with manually identifying aset of related entities, a type of relationship associated with the setof related entities, and/or to identify modifications to a set ofaccounts associated with the set of related entities, as describedherein, are conserved.

FIGS. 1A-1L are diagrams of one or more example implementations 100described herein. As shown, implementation(s) 100 includes a serverdevice and a processing platform. While the processing platform is shownas a device, in some implementations, the processing platform may beprovided using a cloud computing environment, as described in moredetail elsewhere herein.

In some implementations, the processing platform may obtain transactioninformation associated with a plurality of entities. For example, asshown in FIG. 1A, and by reference number 102, the processing platformmay receive transaction information associated with a plurality ofentities from a server device. As shown in FIG. 1A, the processingplatform obtains transaction information from a single server device.However, the implementations described herein are not limited to thosein which transaction information is received from a single serverdevice. For example, the processing platform may receive transactioninformation from a plurality of server devices.

The transaction information may include information identifying orrelating to transactions associated with the plurality of entities. Forexample, the transaction information may include information indicatinga purchase (e.g., of an asset, such as a product, a house, land, etc., aservice, and/or the like), information indicating a sale (e. g., of anasset), financial information associated with a purchase or a sale(e.g., information indicating an amount charged for an item and/orservice, an account of an entity used for the purchase, an invoiceidentifier, a confirmation number, a transaction type, and/or the like),credit card transaction information, checking account transactioninformation, financial software information (e. g., information exportedfrom an entity's financial software), and/or the like.

In some implementations, the processing platform may receive thetransaction information from multiple, different sources (e. g.,multiple accounts, multiple financial institutions, etc.). In someimplementations, the processing platform may retrieve transactioninformation, such as by accessing a user account of an entity usingcredentials associated with the entity. In some implementations, theprocessing platform may receive the transaction information as a datastream. For example, the processing platform may receive transactioninformation as the transactions are performed or processed (e.g.,hundreds of transactions, thousands of transactions, millions oftransactions, etc.), and may perform the operations described herein inreal time as the transactions are performed or processed. In such acase, the processing platform may index information regarding processedtransaction information that can be used to identify selected sets oftransactions, as described in more detail below. This may conservestorage or memory resources of the processing platform that wouldotherwise be used to store batches of transaction information.

In some implementations, the processing platform may perform naturallanguage processing on the transaction information so that thetransaction information is in a machine-readable format. For example,the processing platform may perform natural language processing on thetransaction information to generate natural language processing resultsand may analyze the natural language processing results to identifyinformation included in the transaction information. Natural languageprocessing involves techniques performed (e. g., by a computer systemsuch as, for example, the processing platform) to analyze, understand,and derive meaning from human language in a useful way. Natural languageprocessing can be applied to analyze text, allowing machines tounderstand how humans speak, enabling real world applications such asautomatic text summarization, sentiment analysis, topic extraction,named entity recognition, parts-of-speech tagging, relationshipextraction, stemming, and/or the like.

In some implementations, the processing platform may obtain entityinformation associated with the plurality of entities. For example, asshown in FIG. 1A, and by reference number 104, the processing platformmay receive entity information associated with the plurality of entitiesfrom a server device. The entity information may include informationassociated with an entity, such as location information (e.g., anaddress of the entity, a jurisdiction in which the entity is located,locations associated with transactions associated with the entity,etc.), information identifying entities associated with the entity(e.g., a family member associated with the entity, a representative ofthe entity, an attorney of the entity, etc.), information identifyingaccounts associated with the entity (e.g., accounts associated withtransaction information, etc.), communications associated with theentity (e. g., email messages, text messages, voice messagetranscriptions, etc.), membership information (e. g., identifying anorganization of which the entity is a member), and/or the like.

In some implementations, the processing platform may receive the entityinformation based on a user input. For example, the entity or anotheruser may input, via a user interface associated with the processingplatform, the server device, a user device, and/or another device, atleast part of the entity information or may provide input indicatingwhether entity information determined by the processing platform isaccurate.

In some implementations, the processing platform may retrieve the entityinformation. For example, the processing platform may retrieve theentity information based on accessing an account associated with anentity using credentials associated with the entity (e. g., aftergaining permission from the entity to access the account). In someimplementations, the processing platform may receive the entityinformation as a data stream. For example, when the entity informationrelates to communications, the processing platform may receive theentity information as communications are performed and may perform theoperations described herein in real time as the entity information isreceived. In such a case, the processing platform may index informationdetermined based on the entity information, which may be used toidentify sets of transactions and/or potential modifications to a set ofaccounts, as described in more detail elsewhere herein. Thus, storage ormemory resources of the processing platform that would otherwise be usedto store large datasets of entity information may be conserved.

In some implementations, the processing platform may obtain entityinformation associated with transaction information. For example, theprocessing platform may determine that a transaction, included in thetransaction information is associated with a particular entity. Theprocessing platform may retrieve a set of entity information associatedwith the particular entity based on the particular entity beingassociated with the transaction.

In some implementations, the processing platform may obtain the entityinformation based on a date associated with transaction information. Forexample, the processing platform may obtain entity informationassociated with a range of dates that matches a range of dates of thetransaction information. As another example, the processing platform mayidentify a particular transaction (e.g., based on a size of thetransaction, a dollar amount associated with the transaction, acounterparty of the transaction, a location associated with thetransaction, and/or the like), and may obtain entity informationpertinent to that transaction (e.g., entity information associated withan entity performing the transaction, communications associated with theparticular transaction, etc.). Thus, the processing platform mayidentify particular entity information that is to be retrieved, therebyreducing the volume of entity information to be processed by theprocessing platform and conserving resources of the processing platform.

In some implementations, the processing platform may perform naturallanguage processing on the entity information so that the entityinformation is in a machine-readable format. For example, the processingplatform may perform natural language processing on the entityinformation to generate natural language processing results and mayanalyze the natural language processing results to identify informationincluded in the entity information. Natural language processing involvestechniques performed (e. g., by a computer system) to analyze,understand, and derive meaning from human language in a useful way.Natural language processing can be applied to analyze text, allowingmachines to understand how humans speak, enabling real worldapplications such as automatic text summarization, sentiment analysis,topic extraction, named entity recognition, parts-of-speech tagging,relationship extraction, stemming, and/or the like.

In some implementations, the processing platform may determine a relatedset of entities and/or a type of relationship associated with therelated set of entities (e.g., a marriage relationship, a legal union, aparent-child relationship, a legal guardianship, a partnershiprelationship, a business relationship, and/or another type ofrelationship) based on the transaction information and the entityinformation. For example, the processing platform may process thetransaction information and/or the entity information to determine arelated set of entities and a type of relationship associated with therelated set of entities.

In some implementations, the processing platform may utilize machinelearning to determine the related set of entities. For example, as shownin FIG. 1B, and by reference number 106, the processing platform mayprocess the transaction information, the entity information, and/orinformation identifying an event, a theme, and/or a transactionparameter using a model, shown in FIG. 1B as a related entity model, todetermine a related set of entities and a type of relationshipassociated with the related set of entities. In some implementations,the related entity model may be referred to as a first model. Asdescribed herein, the processing platform may use one or more artificialintelligence techniques, such as machine learning, deep learning, and/orthe like to train the related entity model to determine the sets ofrelated entities based on transaction information, entity information,event information, a theme, and/or a transaction parameter.

In some implementations, the transaction information may includeinformation associated with a plurality of transactions associated withthe plurality of entities. The processing platform may process thetransaction information to determine an event (e.g., a wedding, anengagement party, a baby shower, a major purchase and/or sale (e.g., apurchase and/or sale of a house, an automobile, a purchase and/or saleexceeding a certain dollar amount, and/or another type of major purchaseand/or sale), a theme (e.g., a transaction indicating an establishedrelationship, a transaction indicating a formation of a relationship, atransaction indicating an entity is associated with a particular type ofrelationship (e.g., a marriage relationship, a parent-childrelationship, a business relationship, and/or another type ofrelationship), and/or another type of theme), and/or a transactionparameter (e.g., an amount paid, an amount received, a date of thetransaction, a time of the transaction, an entity conducting thetransaction, an entity associated with, but not conducting, thetransaction, and/or another type of transaction parameter) associatedwith each transaction. For example, the transaction information mayinclude information identifying first transaction associated with apurchase of a wedding dress by a first entity. The processing platformmay associate the first transaction and/or the first entity with anevent (e.g., a life event, a wedding, and/or another event associatedwith a purchase of a wedding dress), a theme (e.g., marriage, legalunion, and/or another theme associated with a purchase of a weddingdress), and/or a transaction parameter (e.g., a store at which thewedding dress was purchased, a price of the wedding dress, a range ofprices that includes a price of the wedding dress, a date of thepurchase, a time that the purchase was made, and/or another type oftransaction parameter).

In some implementations, the processing platform may determine the setof related entities based on the event, the theme, and/or thetransaction parameter associated with each transaction. For example, theprocessing platform may determine that a first entity and a secondentity are both associated with one or more transactions associated witha particular event, a particular theme, and/or a particular transactionparameter. The processing platform may determine that the first entityand the second entity are, or are planning to be, related based on thefirst entity and the second entity both being associated with one ormore transactions associated with the particular event, the particulartheme, and/or the particular transaction parameter.

In some implementations, the processing platform may determine that thefirst entity and the second entity are, or are planning to be, relatedbased on the entity information. For example, the processing platformmay process the entity information to identify first entity informationassociated with the first entity and second entity informationassociated with the second entity. The processing platform may determinethat the first entity and the second entity are, or are planning to be,related based on the first entity information and/or the second entityinformation.

In some implementations, the processing platform may determine whetherthe first entity information and/or the second entity informationsupports, or is consistent with, the determination that the first entityand the second entity are, or are planning to be, related. For example,the first entity information and/or the second entity information mayinclude information indicating a marital status, an age, a location of aprimary residence, an employment status, and/or another type ofinformation suitable for determining whether the first entityinformation and/or the second entity information supports, or isconsistent with, the determination that the first entity and the secondentity are, or are planning to be, related.

In some implementations, the processing platform may obtain accountinformation associated with the set of related entities. For example, asshown in FIG. 1C, and by reference number 108, the processing platformmay obtain account information associated with the related set ofentities from a server device.

In some implementations, the account information may include informationidentifying a set of accounts associated with each individual entityincluded in the set of related entities. For example, the set of relatedentities may include a first entity and a second entity. The accountinformation may include information identifying an account associatedwith the first entity and/or information identifying an accountassociated with the second entity.

In some implementations the account information may include informationidentifying a set of accounts associated with a plurality of entitiesincluded in the related set of entities. For example, the related set ofentities may include a first entity, a second entity, and a thirdentity. The account information may include information identifying anaccount associated with the first entity and the second entity,information identifying an account associated with the first entity andthe third entity, information identifying an account associated with thesecond entity and the third entity, and/or information identifying anaccount associated with the first entity, the second entity, and thethird entity.

In some implementations, the processing platform may determine one ormore modifications to the set of accounts associated with the set ofrelated entities (e.g., one or more accounts to open for the set ofrelated entities, one or more accounts, of the set of accountsassociated with the set of related entities, to cancel, and/or one ormore accounts, of the set of accounts associated with the set of relatedentities, to modify into a joint account) based on a recommended set ofaccounts. For example, as shown in FIG. 1D, and by reference number 110,the processing platform may process entity information associated withthe related set of entities, account information associated with therelated set of entities, and information identifying the type ofrelationship associated with the related set of entities, with a secondmodel, shown in FIG. 1D as an account optimization model, to determine arecommended set of accounts for the set of related entities and/orinformation associated with the recommended set of accounts. Theprocessing platform may determine one or more modifications to the setof accounts associated with the related set of entities based on therecommended set of accounts and/or the information associated with therecommended set of accounts. As described herein, the processingplatform may use one or more artificial intelligence techniques, such asmachine learning, deep learning, and/or the like to train the relatedentity model to determine a recommended set of accounts for the set ofrelated entities and/or information associated with the recommended setof accounts.

In some implementations, the processing platform may determine the oneor more accounts to open, the one or more accounts to cancel, and/or theone or more accounts to modify into a joint account based on an accountcharacteristic (e.g., an interest rate, a minimum balance to bemaintained, a fee, a rewards program, an outstanding balance, a creditlimit, and/or another account characteristic). For example, the set ofaccounts associated with the set of related entities may include a firstcredit card account associated with a first entity, of the set ofrelated entities, and a second credit card account associated with asecond entity, of the set of related entities. The processing platformmay determine that an interest rate associated with the first creditcard account is lower than an interest rate associated with the secondcredit card account. The processing platform may determine to modify thefirst credit card account to form a joint account associated with thefirst entity and the second entity and/or to close the second creditcard account based on the interest rate associated with the first creditcard account being lower than the interest rate associated with thesecond credit card account.

In some implementations, the processing platform may determine the oneor more accounts to open, the one or more accounts to cancel, and/or theone or more accounts to modify into a joint account based on asocioeconomic factor (e.g., income, education, occupation, net worth,credit score, and/or another socioeconomic factor) associated with oneor more entities included in the set of related entities. For example,the processing platform may determine sets of accounts associated withother sets of related entities associated with the same type ofrelationship and/or having a similar or corresponding socioeconomicfactor as the set of related entities. The processing platform maydetermine the one or more accounts to open, the one or more accounts tocancel, and/or the one or more accounts to modify into a joint accountbased on the sets of accounts associated with the other sets of relatedentities.

In some implementations, the processing platform may determine arecommended set of accounts based on the sets of accounts associatedwith the other sets of related entities. For example, the processingplatform may identify a type of account (e.g., savings, checking,brokerage, and/or another type of account) associated with each accountincluded in the sets of accounts associated with the other sets ofrelated entities. For each type of account identified, the processingplatform may determine a commonality factor associated with the type ofaccount. The commonality factor may represent a quantity and/or apercentage of sets of accounts associated with the other sets of relatedentities having that type of account. The processing platform maydetermine to include the type of account in the recommended set ofaccounts based on the commonality factor. In some implementations, theprocessing platform may compare the commonality factor, the quantity,and/or the percentage to a threshold commonality factor, a thresholdquantity, and/or a threshold percentage. The processing platform maydetermine to include the type of account in the recommended set ofaccounts when commonality factor, the quantity, and/or the percentage isgreater than, or equal to, the threshold commonality factor, thethreshold quantity, and/or the threshold percentage.

The processing platform may determine the one or more accounts to open,the one or more accounts to cancel, and/or the one or more accounts tomodify into a joint account based on the recommended set of accounts.For example, the processing platform may compare the set of accountsassociated with the set of related entities to the recommended set ofaccounts. The processing platform may determine that the set of accountsassociated with the set of related entities does not include a type ofaccount included in the recommended set of accounts. In someimplementations, the processing platform may determine to open the typeof account included in the recommended set of accounts based on the setof accounts associated with the set of related entities not includingthe type of account. For example, the type of account may be a brokerageaccount. The processing platform may determine to open a brokerageaccount for the set of related entities based on the brokerage accountbeing included in the recommended set of accounts.

In some implementations, the processing platform may determine to closean account included in the set of accounts associated with the relatedset of entities. For example, the set of accounts associated with therelated set of entities may include a type of account that is notincluded in the recommended set of accounts. The processing platform maydetermine to close the type of account based on the type of account notbeing included in the recommended set of accounts. As another example,the set of accounts associated with the related set of entities mayinclude multiple, individual video streaming accounts with the samevideo streaming provider. The processing platform may determine to closeone or more of the multiple, individual video streaming accounts basedon the multiple, individual video streaming accounts being with the sameprovider.

In some implementations, the processing platform may determine to modifyan account included in the set of accounts associated with the relatedentities to form a joint account. For example, the processing platformmay determine that the set of accounts associated with the relatedentities includes a type (e.g., a savings account, a checking account, abrokerage account, and/or another type of account) of individual account(e.g., an account associated with only one entity of the set of relatedentities). The processing platform may determine that the recommendedset of accounts includes a joint account (e.g., an account associatedwith two or more entities of the set of related entities) of the sametype. The processing platform may determine to modify the individualaccount to form a joint account based on the recommended set of accountsincluding the joint account of the same type.

In some implementations, as shown in FIG. 1E, the recommended set ofaccounts may include one or more accounts associated with other sets ofrelated entities that are associated with the type of relationshipassociated with the set of related entities and/or one or moresocioeconomic factors associated with the set of related entities. Forexample, the set of related entities may include a first entity marriedto a second entity. The first and second entities may have ages within aparticular range of ages (e.g., 21-25 years old, 26-30 years old, 31-35years old, over the age of 50, and/or the like), may be of a particularethnicity, may live in a particular geographic region, may have anannual income within a particular range of annual incomes (e.g.,$10,000-$50,000, $50,000-$100,000, over $100,000, and/or the like), mayhave a total and/or individual net worth within a particular range ofnet worth values (e.g., $10,000-$50,000, $50,000-$100,000, over$100,000, and/or the like), and/or another socioeconomic factor.

The processing platform may identify other sets of related entities thatare married, have ages within the particular range of ages, are of theparticular ethnicity, live in a particular geographic region, have anannual income within the particular range of annual incomes, and/or havea total and/or individual net worth within the particular range of networth values. The processing platform may determine a set of accountsassociated with each set of related entities included in the other setsof related entities. The processing platform may determine therecommended set of accounts based on the set of accounts associated witheach set of related entities included in the other sets of relatedentities.

In some implementations, the recommended set of accounts may include anaccount corresponding to each type and/or category of account includedin the sets of accounts associated with the other sets of relatedentities. For example, the processing platform may determine that thesets of accounts associated with the other sets of related entitiesincludes one or more savings accounts, one or more checking accounts,one or more brokerage accounts, one or more retirement accounts, and oneor more video streaming service account associated with a particularvideo streaming service provider. The processing platform may generatethe recommended set of accounts to include a savings account, a checkingaccount, a brokerage account, a retirement account, and a videostreaming service account associated with the particular video streamingservice provider.

In some implementations, the recommended set of accounts may includeaccounts corresponding to a type and/or category of account occurring ina particular quantity and/or percentage of accounts included in the setsof accounts associated with the other sets of related entities. Forexample, the processing platform may determine a quantity of accountsthat include a savings account. The processing platform may determinewhether the quantity of accounts is greater than a threshold quantity.The processing platform may include a savings account in the recommendedset of accounts when the quantity of accounts is greater than thethreshold quantity.

In some implementations, as shown in FIG. 1E, the information associatedwith the recommended set of accounts may include, for each accountincluded in the recommended set of accounts, information indicating apercentage of the other sets of related entities for which the accountis a joint account (e.g., associated with at least two related entities)rather than an individual account (e.g., associated with only oneentity). For example, the processing platform may determine a quantityof accounts that include a checking account. The processing platform maydetermine a percentage of the checking accounts that are joint checkingaccounts and/or a percentage of the checking accounts that areindividual checking accounts. The information associated with therecommended set of accounts may include the percentage of the checkingaccounts that are joint checking accounts and/or the percentage of thechecking accounts that are individual checking accounts.

In some implementations, the processing platform may determine that therelated set of entities are associated with corresponding accounts. Thecorresponding accounts may be an account of a particular type and/orcategory and/or associated with a particular account provider (e.g., asavings account, a checking account, an entertainment account, a creditcard account associated with a particular financial institution, and/orthe like) associated with a first entity of the set of related entitiesand an account of the same type and/or category and/or associated withthe same account provider associated with a second entity of the set ofrelated entities.

For example, as shown in FIG. 1E, the account information associatedwith the first entity may include information identifying a first set ofaccounts associated with the first entity. The account informationassociated with the second entity may include information identifying asecond set of accounts associated with the second entity. As shown inFIG. 1E, and by reference number 112, the processing platform maydetermine that an account associated with the first entity (shown inFIG. 1E as Account A) corresponds to an account associated with thesecond entity (also shown in FIG. 1E as Account A).

In some implementations, the processing platform may determine that theaccount associated with the first entity corresponds to the accountassociated with the second entity based on a comparison. For example,the processing platform may compare information associated with thefirst set of accounts and information associated with the second set ofaccounts. The processing platform may determine that the first andsecond entities are associated with corresponding accounts based on thecomparison.

In some implementations, the processing platform may compare identifiersassociated with each account in the first set of accounts andidentifiers associated with each account in the second set of accounts.For example, the information associated with the first set of accountsmay include a first identifier associated with a first account includedin the first set of accounts. The first identifier may indicate a typeor category of account (e.g., banking, savings, checking, entertainment,streaming media service account, news, current events, social media,and/or another type and/or category of account) associated with thefirst account. The processing platform may compare the first identifierand a second identifier associated with a second account included in thesecond set of accounts. The processing platform may determine that thefirst and second accounts are corresponding accounts based on comparingthe first identifier and the second identifier.

Alternatively, and/or additionally, the processing platform may compareanother type of information included in the information associated withthe first set of accounts and/or the information associated with thesecond set of accounts such as, for example, a name of a serviceprovider associated with the account, a name of a financial institutionassociated with the account, a type and/or category of a transactionassociated with the account (e.g., a deposit, a withdrawal, a payment,and/or another type and/or category of a transaction), and/or anothertype of information useful in determining whether the accountcorresponds to an account associated with a related entity.

In some implementations, the processing platform may determine whetherto consolidate one or more of the corresponding accounts. For example,as shown in FIG. 1E, the processing platform may determine toconsolidate Account A of Entity 1 and Account A of Entity 2 based ondetermining that Account A of Entity 1 corresponds to Account A ofEntity 2.

In some implementations, the processing platform may determine whetherto consolidate one or more of the corresponding accounts based on therecommended set of accounts and/or the information associated with therecommended set of accounts. For example, the recommended set ofaccounts may include an Account A and the information associated withthe recommended set of accounts may indicate a quantity and/or apercentage of sets of related entities, included in the other sets ofrelated entities, having a joint Account A rather than one or moreindividual Account A's. As shown in FIG. 1E, and by reference number114, the processing platform may determine a percentage of the sets ofrelated entities, having a joint Account A based on the informationassociated with the recommended set of accounts.

As shown in FIG. 1E, and by reference number 116, the processingplatform may determine that the percentage of sets of related entitiesis greater than a threshold percentage. The processing platform maydetermine to consolidate the Account A of the first entity and theAccount A of the second entity based on the percentage of sets ofrelated entities being greater than the threshold percentage. In someimplementations, as shown in FIG. 1E, and by reference number 118, theprocessing platform may determine to consolidate the correspondingaccounts based on the percentage of the sets of related entities beinggreater than the threshold percentage.

In some implementations, the processing platform may consolidate thecorresponding accounts by modifying one or more accounts of thecorresponding accounts to form a joint account associated with at leasttwo entities included in the set of related entities. For example, theprocessing platform may modify the Account A associated with the firstentity to form a joint account associated with the first entity and thesecond entity.

In some implementations, the processing platform may select the one ormore accounts to be modified based on an entity characteristicassociated with an entity included in the set of related entities. Theentity characteristic may include a credit score of the entity, a networth of the entity, an age of the entity, and/or another characteristicassociated with an entity that may be useful in selecting an account tobe modified to form a joint account. For example, the processingplatform may determine a credit score associated with the first entityand a credit score associated with the second entity based on the entityinformation associated with the first entity and the entity associatedwith the second entity, respectively. The processing platform maydetermine that the credit score associated with the first entity ishigher than the credit score associated with the second entity. Theprocessing platform may select the Account A associated with the firstentity as the account to be modified to form the joint account (ratherthan the Account A associated with the second entity) based on thecredit score associated with the first entity being higher than thecredit score associated with the second entity.

Alternatively, and/or additionally, the processing platform may selectthe one or more accounts to be modified based on an accountcharacteristic associated with one of the corresponding accounts. Theaccount characteristic may include an interest rate associated with oneof the corresponding accounts, a rewards program associated with one ofthe corresponding accounts, a rating associated with a service provider(e.g., a consumer satisfaction rating, a reliability rating, and/oranother type of rating) associated with one of the correspondingaccounts, a value of an asset associated with one of the correspondingaccounts, a value of a liability associated with one of thecorresponding accounts, and/or another characteristic associated with anaccount that may be useful in selecting an account to be modified toform a joint account. For example, the Account A associated with thefirst entity and the Account A associated with the second entity may besavings accounts. The processing platform may determine an interest rateassociated with the Account A associated with the first entity and aninterest rate associated with the Account A associated with the secondentity based on the account information associated with the first entityand the account information associated with the second entity,respectively. The processing platform may determine that the interestrate associated with the Account A associated with the first entity ishigher than the interest rate associated with the Account A associatedwith the second entity. The processing platform may select the Account Aassociated with the first entity as the account to be modified to formthe joint account (rather than the Account A associated with the secondentity) based on the interest rate associated with the Account Aassociated with the first entity being higher than the interest rateassociated with the Account A associated with the second entity.

In some implementations, the processing platform may determine to cancelone or more of the corresponding accounts based on determining to modifyanother one or more of the corresponding accounts. For example, theprocessing platform may determine to modify the Account A associatedwith the first entity to form a joint account associated with the firstentity and the second entity. The processing platform may determine tocancel the Account A associated with the second entity based onmodifying the Account A associated with the first entity to form thejoint account. To cancel the Account A of the second entity, theprocessing platform may send instructions to a server, associated withthe Account A of the second entity, to cancel the account.

In some implementations, the processing platform may determine not toconsolidate corresponding accounts. For example, as shown in FIG. 1F,and by reference number 120, the processing platform may determine thatAccount B of entity 1 corresponds to Account B of entity 1. In someimplementations, the processing platform may determine that Account B ofentity 1 corresponds to Account B of entity 1 in a manner similar tothat described above with respect to FIG. 1E.

As shown in FIG. 1F, and by reference number 122, the processingplatform may determine a percentage of the sets of related entities,included in the other sets of related entities having a joint Account Brather than one or more individual Account B's (shown in FIG. 1F as 17%)based on the information associated with the set of accounts associatedwith the other sets of related entities.

As shown in FIG. 1F, and by reference number 124, the processingplatform may determine that the percentage of the sets of relatedentities is less than a threshold percentage. As shown in FIG. 1F, andby reference number 126, the processing platform may determine not toconsolidate the Account B of the first entity and the Account by of thesecond entity based on the percentage of the sets of related entitieshaving a joint account B being less than the threshold percentage.

In some implementations, the processing platform may determine toconsolidate one or more accounts associated with the set of relatedentities based on a quantity of accounts. For example, the processingplatform may determine to consolidate one or more accounts associatedwith the set of related entities based on a quantity of accountsassociated with a particular type and/or category of account beinggreater than a threshold quantity of accounts associated with theparticular type and/or category of account.

In some implementations, the processing platform may form subsets ofaccounts based on a type and/or category associated with each accountassociated with the first entity and/or the second entity. For example,as shown in FIG. 1G, the processing platform may determine a categoryassociated with each account associated with the first entity and acategory associated with each account associated with the second entity.

As shown in FIG. 1G, and by reference number 128, the processingplatform may generate subsets of accounts based on the category. Forexample, the processing platform may generate the subsets of accounts bygrouping accounts associated with the same type and/or category intodiscrete subsets of accounts.

In some implementations, each category may be associated with athreshold quantity. The threshold quantity may be based on a quantity ofaccounts associated with each category that are included in the sets ofaccounts associated with other sets of related entities associated withthe type of relationship associated with the set of related entities.For example, the other sets of related entities may include a first setof related entities and a second set of related entities. The processingplatform may determine that the first set of related entities isassociated with 3 entertainment accounts (e.g., an Audible® account, aNetflix® account, and a Hulu® account) and that the second set ofrelated entities is associated with 5 entertainment accounts (e.g., twoAudible® accounts, a Netflix® account, a Hulu® account, and a Roku®account). The processing platform may determine a threshold quantityassociated with the particular category based on the first quantity(e.g., 3) and/or the second quantity (e.g., 5). In some implementations,the threshold quantity may be the average of the first quantity and thesecond quantity (e.g., 4), the greater of the first quantity and thesecond quantity (e.g., 5), or the lesser of the first quantity and thesecond quantity (e.g., 3).

Alternatively, and/or additionally, the threshold quantity may bedetermined in another manner such as a frequency of a quantity ofaccounts associated with the particular category. For example, theprocessing platform may determine that a majority of the other sets ofrelated entities have 4 entertainment accounts. The processing platformmay determine the threshold quantity is 4 based on the majority of theother sets of related entities having 4 entertainment accounts.

In some implementations, the processing platform may determine that aquantity of accounts, included in a subset of accounts, is greater thana threshold quantity. For example, as shown in FIG. 1G, and by referencenumber 130, the processing platform may determine that a quantity ofaccounts included in Subset 2 is greater than a threshold quantityassociated with a category associated with Subset 2 (e.g., StreamingServices, as shown in FIG. 1G). As shown in FIG. 1G, and by referencenumber 132, the processing platform may determine to close one or moreaccounts included in the subset of accounts based on the quantity ofaccounts being greater than the threshold quantity.

In some implementations, as shown in FIG. 1H, and by reference number134, the processing platform may perform one or more actions based ondetermining to modify and/or close one or more accounts associated withthe set of related entities.

In some implementations, the one or more actions may include providing arecommendation to close one or more accounts and/or to modify one ormore accounts to form a joint account to one or more entities includedin the set of related entities, receiving a response to therecommendation, and/or performing an action based on the response. Forexample, the processing platform may generate a recommendation to closeone or more accounts and/or to modify one or more accounts to form ajoint based on determining that an account associated with one of theentities, of the set of related entities, corresponds to an accountassociated with another one of the entities, of the set of relatedentities. The processing platform may transmit the recommendation to oneor more user devices associated with the set of related entities. Theprocessing platform may receive a response from the set of relatedentities (e.g., from the one or more user devices associated with theset of related entities). The response may indicate that the processingplatform is to close the one or more accounts and/or modify the one ormore accounts. The processing platform may close the one or moreaccounts and/or modify the one or more accounts based on the response.

In some implementations, the one or more actions may includeautomatically providing a link (e.g., a uniform resource locator (URL))for communicating with (e.g., via a telephone call, an email message, anInstant Message, a text message, and/or the like) an account provider toone or more user devices associated with the set of related entities.For example, the recommendation may include a link for establishing atelephone call with an account provider associated with the one or moreaccounts recommended to be closed and/or a link for establishing atelephone call with an account provider associated with the one or moreaccounts recommended to be modified to form the one or more jointaccounts.

Alternatively, and/or additionally, the action performed based on theresponse may include providing the link for establishing the telephonecall with the account provider associated with the one or more accountsrecommended to be closed and/or the link for establishing the telephonecall with an account provider associated with the one or more accountsrecommended to be modified to form the one or more joint accounts. Forexample, the response may indicate that the set of related entities willclose the one or more accounts recommended to be closed and/or willmodify the one or more accounts recommended to be modified to form theone or more joint accounts. The processing platform may provide the linkfor establishing the telephone call with the account provider associatedwith the one or more accounts recommended to be closed and/or the linkfor establishing the telephone call with an account provider associatedwith the one or more accounts recommended to be modified to form the oneor more joint accounts to one or more user devices associated with theset of related entities based on the response.

In some implementations, the recommendation may include informationidentifying a subset of accounts associated with a particular categoryand/or a recommendation to close and/or modify one or more accountsincluded in the subset of accounts and the response may includeinformation identifying a first group of one or more accounts includedin the subset of accounts that are to be closed and/or informationidentifying a second group of one or more accounts included in thesubset of accounts to modified to form one or more joint accounts. Inthese implementations, the action performed based on the response mayinclude closing the first group of accounts and/or modifying the secondgroup of accounts to form the one or more joint accounts.

In some implementations, the one or more actions may includeautomatically closing one or more accounts associated with the set ofrelated entities and/or automatically modifying one or more accountsassociated with the set of related entities to form one or more jointaccounts. For example, continuing with the example discussed above withrespect to FIG. 1E, the processing platform may determine to consolidatethe Account A of entity 1 and the Account A of entity 2 based on thepercentage of the sets of related entities having a joint Account A isgreater than a first threshold percentage.

The processing platform may determine whether the percentage of the setsof related entities having a joint Account A is greater than a secondthreshold percentage based on the percentage of the sets of relatedentities having a joint Account A being greater than the first thresholdpercentage. The second threshold percentage may be greater than thefirst threshold percentage and may be associated with automaticallyperforming an action. The processing platform may determine toautomatically consolidate the Account A associated with the first entityand the Account A associated with the second entity by modifying theAccount A associated with the first entity to form a joint account andclosing the Account A associated with the second entity based on thepercentage of the sets of related entity having a joint Account A beinggreater than the second threshold percentage.

In some implementations, the processing platform may determine anoccurrence of a life event (e.g., a birth of a child, an adoption of achild, a death of a family member, a high school graduation, a collegegraduation, an event associated with an end to the relationship betweenthe set of related entities (e.g., a divorce, a death of one of theentities, a dissolution of a business, and/or another type of eventassociated with an end to the relationship between the set of relatedentities), and/or another type of life event) associated with the set ofrelated entities.

In some implementations, the processing platform may determine theoccurrence of the life event based on transaction information and/orentity information associated with the set of related entities. Forexample, as shown in FIG. 1I, and by reference numbers 136 and 138, theprocessing platform may obtain transaction information and entityinformation associated with the set of related entities from a serverdevice.

The transaction information associated with the set of related entitiesmay include transaction information associated with one or more of theentities included in the set of related entities. For example, as shownin FIG. 1I, the transaction information associated with the set ofrelated entities includes transaction information associated with afirst entity (shown as Entity 1) and transaction information associatedwith a second entity (shown as Entity 2) of the set of related entities.

The entity information associated with the set of related entities mayinclude entity information associated with one or more of the entitiesincluded in the set of related entities and/or information identifyingthe type of relationship associated with the set of related entities.For example, as shown in FIG. 1I, the entity information associated withthe set of related entities may include entity information associatedwith the first entity, entity information associated with the secondentity, and information identifying the type of relationship associatedwith the set of related entities.

In some implementations, the processing platform may obtain the entityinformation associated with the set of related entities based on thetransaction information. For example, the processing platform mayanalyze the transaction information that a first transaction wasconducted by the first entity and that a second transaction wasconducted by the second entity. The processing device may obtain theentity information associated with the first entity based on the firsttransaction being conducted by the first entity. The processing platformmay obtain the entity information associated with the second entitybased on the second transaction being conducted by the second entity.

In some implementations, as shown in FIG. 1J, and by reference number140, the processing platform may process the transaction information,the entity information, and information identifying the type ofrelationship associated with the set of related entities, with a thirdmodel, shown in FIG. 1J as a life event occurrence model, to determinethe occurrence of the life event. As described herein, the processingplatform may use one or more artificial intelligence techniques, such asmachine learning, deep learning, and/or the like to train the relatedentity model to determine the occurrence of the life event.

In some implementations, the processing platform may determine a set oflife events associated with sets of related entities associated with thetype of relationship associated with the set of related entities. Forexample, the processing platform may determine that the type ofrelationship (e.g., married) and one or more socioeconomic factorsassociated with the set of related entities. The processing platform maydetermine a set of life events associated with the set of relatedentities based on the type of relationship and the one or moresocioeconomic factors.

In some implementations, the processing platform may determine the setof life events based on sets of related entities associated with thesame type of relationship and/or associated with the same, or similar,socioeconomic factors as the set of related entities. For example, theprocessing platform may identify sets of related entities associatedwith the same type of relationship and/or the one or more socioeconomicfactors associated with the set of related entities. The processingplatform may analyze entity information associated with the identifiedsets of related entities to determine one or more life eventsexperienced by, or associated with, the identified sets of relatedentities. The processing platform may determine the set of life eventsassociated with the set of related entities based on the one or morelife events experienced by, or associated with, the identified sets ofrelated entities.

In some implementations, the processing platform may determine the setof life events associated with the set of related entities based on aquantity and/or a percentage of the identified sets of related entitieshaving experienced a particular life event. For example, the processingplatform may determine whether a quantity and/or a percentage of theidentified sets of related entities that have experienced the particularlife event is greater than, or equal to, a threshold quantity and/or athreshold percentage. The processing platform may include the particularlife event in the set of life events associated with the set of relatedentities when the quantity and/or the percentage of the identified setsof related entities that have experienced the particular life event isgreater than, or equal to, the threshold quantity and/or the thresholdpercentage.

In some implementations, the processing platform may include each lifeevent experienced by the identified sets of related entities in the setof life events associated with the set of related entities. For example,the processing platform may determine that a quantity of sets of relatedentities, included in the identified sets of related entities is lessthan a threshold quantity. The processing platform may include each lifeevent experienced by the identified sets of related entities in the setof life events associated with the set of related entities based on thequantity of the sets of related entities being less than the thresholdquantity.

In some implementations, the processing platform may determine theoccurrence of the life event based on the set of life events. Forexample, the processing platform may determine that one or moretransactions included in the transaction information associated with theset of related entities are associated with a particular life eventincluded in the set of life events. The processing platform maydetermine the occurrence of the particular life event based on the oneor more transactions being associated with the particular life event.

In some implementations, as shown in FIG. 1K, and by reference number142, the processing platform may modify the entity informationassociated with the set of related entities based on the occurrence ofthe life event. For example, the set of related entities may include afirst entity and a second entity and the life event may be the birth ofa child. The processing platform may modify the entity informationassociated with the first entity and/or the entity informationassociated with the second entity to include information identifying thefirst entity and/or the second entity as having a (or another)dependent, as being a parent of the child, and/or another type ofinformation associated with the life event.

In some implementations, the processing platform may determine one ormore modifications to the set of accounts associated with the set ofrelated entities based on the modified entity information associatedwith the set of related entities. For example, as shown in FIG. 1K, andby reference number 144, the processing platform may process accountinformation associated with the set of related entities and the modifiedentity information associated with the set of related entities, with theaccount optimization model, to determine the one or more modifications.In some implementations, the processing platform may determine the oneor more modifications in a manner similar to the method described abovewith regards to FIG. 1D.

In some implementations, the processing platform may determine sets ofaccounts associated with sets of other related entities having similarentity information (e.g., similar socioeconomic factors) and/or havingan occurrence a same, similar, and/or corresponding life eventexperienced by the set of related entities. The processing platform maydetermine the one or more modifications based on the sets of accountsassociated with the sets of other related entities. For example, theprocessing platform may analyze the sets of accounts associated with thesets of other related entities and may determine a modification made toone or more of the sets of accounts based on having an occurrence of thesame, similar, and/or corresponding life event and may determine tomodify the set of accounts associated with the set of related entitiesin a similar manner.

In some implementations, the processing platform may perform one or moreactions based on determining the one or more modifications to the set ofaccounts associated with the related entities. For example, as shown inFIG. 1L, and by reference number 146, the processing platform mayperform one or more actions based on the output of the accountoptimization model. In some implementations, the one or more actionsperformed by the processing platform may be similar to the one or moreactions described above with regards to FIG. 1H.

As indicated above, FIGS. 1A-L are provided as one or more examples.Other examples may differ from what is described with regard to FIGS.1A-L.

FIG. 2 is a diagram illustrating an example 200 of training a machinelearning model. The machine learning model training described herein maybe performed using a machine learning system. The machine learningsystem may include a computing device, a server, a cloud computingenvironment, and/or the like, such as processing platform 810, asdescribed elsewhere herein.

As shown by reference number 205, a machine learning model may betrained using a set of observations. The set of observations may beobtained and/or input from historical data, such as data gathered duringone or more processes described herein. For example, the set ofobservations may include data gathered from the server device 805, asdescribed elsewhere herein. In some implementations, the machinelearning system may receive the set of observations (e.g., as input)from the processing platform 810.

As shown by reference number 210, a feature set may be derived from theset of observations. The feature set may include a set of variabletypes. A variable type may be referred to as a feature. A specificobservation may include a set of variable values corresponding to theset of variable types. A set of variables values may be specific to anobservation. In some cases, different observations may be associatedwith different sets of variable values, sometimes referred to as featurevalues.

In some implementations, the machine learning system may determinevariable values for a specific observation based on input received fromthe server device 805 and/or the processing platform 810. For example,the machine learning system may identify a feature set (e.g., one ormore features and/or corresponding feature values) from structured datainput to the machine learning system, such as by extracting data from aparticular column of a table, extracting data from a particular field ofa form, extracting data from a particular field of a message, extractingdata received in a structured data format, and/or the like.

In some implementations, the machine learning system may determinefeatures (e.g., variables types) for a feature set based on inputreceived from the server device 805 and/or the processing platform 810,such as by extracting or generating a name for a column, extracting orgenerating a name for a field of a form and/or a message, extracting orgenerating a name based on a structured data format, and/or the like.Additionally, or alternatively, the machine learning system may receiveinput from an operator to determine features and/or feature values. Insome implementations, the machine learning system may perform naturallanguage processing and/or another feature identification technique toextract features (e.g., variable types) and/or feature values (e.g.,variable values) from text (e.g., unstructured data) input to themachine learning system, such as by identifying keywords and/or valuesassociated with those keywords from the text.

As an example, a feature set for a set of observations may include afirst feature of event, a second feature of theme, a third feature oftransaction parameter, and so on. As shown, for a first observation, thefirst feature may have a value of new car (e.g., the purchase of a newcar), the second feature may have a value of major purchase, the thirdfeature may have a value of price (e.g., a purchase price of the newcar), and so on. These features and feature values are provided asexamples, and may differ in other examples. For example, the feature setmay include one or more of the following features: a feature indicatingan established relationship, a feature indicating a formation of arelationship, a feature indicating an entity conducting a transaction, afeature indicating an entity associated with, but not conducting, thetransaction, and/or another feature suitable for determining that anentity is related to another entity. In some implementations, themachine learning system may pre-process and/or perform dimensionalityreduction to reduce the feature set and/or combine features of thefeature set to a minimum feature set. A machine learning model may betrained on the minimum feature set, thereby conserving resources of themachine learning system (e.g., processing resources, memory, and/or thelike) used to train the machine learning model.

As shown by reference number 215, the set of observations may beassociated with a target variable type. The target variable type mayrepresent a variable having a numeric value (e.g., an integer value, afloating point value, and/or the like), may represent a variable havinga numeric value that falls within a range of values or has some discretepossible values, may represent a variable that is selectable from one ofmultiple options (e.g., one of multiples classes, classifications,labels, and/or the like), may represent a variable having a Booleanvalue (e.g., 0 or 1, True or False, Yes or No), and/or the like. Atarget variable type may be associated with a target variable value, anda target variable value may be specific to an observation. In somecases, different observations may be associated with different targetvariable values.

The target variable may represent a value that a machine learning modelis being trained to predict, and the feature set may represent thevariables that are input to a trained machine learning model to predicta value for the target variable. The set of observations may includetarget variable values so that the machine learning model can be trainedto recognize patterns in the feature set that lead to a target variablevalue. A machine learning model that is trained to predict a targetvariable value may be referred to as a supervised learning model, apredictive model, and/or the like. When the target variable type isassociated with continuous target variable values (e.g., a range ofnumbers and/or the like), the machine learning model may employ aregression technique. When the target variable type is associated withcategorical target variable values (e.g., classes, labels, and/or thelike), the machine learning model may employ a classification technique.

In some implementations, the machine learning model may be trained on aset of observations that do not include a target variable (or thatinclude a target variable, but the machine learning model is not beingexecuted to predict the target variable). This may be referred to as anunsupervised learning model, an automated data analysis model, anautomated signal extraction model, and/or the like. In this case, themachine learning model may learn patterns from the set of observationswithout labeling or supervision, and may provide output that indicatessuch patterns, such as by using clustering and/or association toidentify related groups of items within the set of observations.

As further shown, the machine learning system may partition the set ofobservations into a training set 220 that includes a first subset ofobservations, of the set of observations, and a test set 225 thatincludes a second subset of observations of the set of observations. Thetraining set 220 may be used to train (e.g., fit, tune, and/or the like)the machine learning model, while the test set 225 may be used toevaluate a machine learning model that is trained using the training set220. For example, for supervised learning, the test set 225 may be usedfor initial model training using the first subset of observations, andthe test set 225 may be used to test whether the trained modelaccurately predicts target variables in the second subset ofobservations. In some implementations, the machine learning system maypartition the set of observations into the training set 220 and the testset 225 by including a first portion or a first percentage of the set ofobservations in the training set 220 (e.g., 75%, 80%, or 85%, amongother examples) and including a second portion or a second percentage ofthe set of observations in the test set 225 (e.g., 25%, 20%, or 15%,among other examples). In some implementations, the machine learningsystem may randomly select observations to be included in the trainingset 220 and/or the test set 225.

As shown by reference number 230, the machine learning system may traina machine learning model using the training set 220. This training mayinclude executing, by the machine learning system, a machine learningalgorithm to determine a set of model parameters based on the trainingset 220. In some implementations, the machine learning algorithm mayinclude a regression algorithm (e.g., linear regression, logisticregression, and/or the like), which may include a regularized regressionalgorithm (e.g., Lasso regression, Ridge regression, Elastic-Netregression, and/or the like). Additionally, or alternatively, themachine learning algorithm may include a decision tree algorithm, whichmay include a tree ensemble algorithm (e.g., generated using baggingand/or boosting), a random forest algorithm, a boosted trees algorithm,and/or the like. A model parameter may include an attribute of a machinelearning model that is learned from data input into the model (e.g., thetraining set 220). For example, for a regression algorithm, a modelparameter may include a regression coefficient (e.g., a weight). For adecision tree algorithm, a model parameter may include a decision treesplit location, as an example.

As shown by reference number 235, the machine learning system may useone or more hyperparameter sets 240 to tune the machine learning model.A hyperparameter may include a structural parameter that controlsexecution of a machine learning algorithm by the machine learningsystem, such as a constraint applied to the machine learning algorithm.Unlike a model parameter, a hyperparameter is not learned from datainput into the model. An example hyperparameter for a regularizedregression algorithm includes a strength (e.g., a weight) of a penaltyapplied to a regression coefficient to mitigate overfitting of themachine learning model to the training set 220. The penalty may beapplied based on a size of a coefficient value (e.g., for Lassoregression, such as to penalize large coefficient values), may beapplied based on a squared size of a coefficient value (e.g., for Ridgeregression, such as to penalize large squared coefficient values), maybe applied based on a ratio of the size and the squared size (e.g., forElastic-Net regression), may be applied by setting one or more featurevalues to zero (e.g., for automatic feature selection), and/or the like.Example hyperparameters for a decision tree algorithm include a treeensemble technique to be applied (e.g., bagging, boosting, a randomforest algorithm, a boosted trees algorithm, and/or the like), a numberof features to evaluate, a number of observations to use, a maximumdepth of each decision tree (e.g., a number of branches permitted forthe decision tree), a number of decision trees to include in a randomforest algorithm, and/or the like.

To train a machine learning model, the machine learning system mayidentify a set of machine learning algorithms to be trained (e.g., basedon operator input that identifies the one or more machine learningalgorithms, based on random selection of a set of machine learningalgorithms, and/or the like), and may train the set of machine learningalgorithms (e.g., independently for each machine learning algorithm inthe set) using the training set 220. The machine learning system maytune each machine learning algorithm using one or more hyperparametersets 240 (e.g., based on operator input that identifies hyperparametersets 240 to be used, based on randomly generating hyperparameter values,and/or the like). The machine learning system may train a particularmachine learning model using a specific machine learning algorithm and acorresponding hyperparameter set 240. In some implementations, themachine learning system may train multiple machine learning models togenerate a set of model parameters for each machine learning model,where each machine learning model corresponds to a different combinationof a machine learning algorithm and a hyperparameter set 240 for thatmachine learning algorithm.

In some implementations, the machine learning system may performcross-validation when training a machine learning model. Crossvalidation can be used to obtain a reliable estimate of machine learningmodel performance using only the training set 220, and without using thetest set 225, such as by splitting the training set 220 into a number ofgroups (e.g., based on operator input that identifies the number ofgroups, based on randomly selecting a number of groups, and/or the like)and using those groups to estimate model performance. For example, usingk-fold cross-validation, observations in the training set 220 may besplit into k groups (e.g., in order or at random). For a trainingprocedure, one group may be marked as a hold-out group, and theremaining groups may be marked as training groups. For the trainingprocedure, the machine learning system may train a machine learningmodel on the training groups and then test the machine learning model onthe hold-out group to generate a cross-validation score. The machinelearning system may repeat this training procedure using differenthold-out groups and different test groups to generate a cross-validationscore for each training procedure. In some implementations, the machinelearning system may independently train the machine learning model ktimes, with each individual group being used as a hold-out group onceand being used as a training group k−1 times. The machine learningsystem may combine the cross-validation scores for each trainingprocedure to generate an overall cross-validation score for the machinelearning model. The overall cross-validation score may include, forexample, an average cross-validation score (e.g., across all trainingprocedures), a standard deviation across cross-validation scores, astandard error across cross-validation scores, and/or the like.

In some implementations, the machine learning system may performcross-validation when training a machine learning model by splitting thetraining set into a number of groups (e.g., based on operator input thatidentifies the number of groups, based on randomly selecting a number ofgroups, and/or the like). The machine learning system may performmultiple training procedures and may generate a cross-validation scorefor each training procedure. The machine learning system may generate anoverall cross-validation score for each hyperparameter set 240associated with a particular machine learning algorithm. The machinelearning system may compare the overall cross-validation scores fordifferent hyperparameter sets 240 associated with the particular machinelearning algorithm, and may select the hyperparameter set 240 with thebest (e.g., highest accuracy, lowest error, closest to a desiredthreshold, and/or the like) overall cross-validation score for trainingthe machine learning model. The machine learning system may then trainthe machine learning model using the selected hyperparameter set 240,without cross-validation (e.g., using all of data in the training set220 without any hold-out groups), to generate a single machine learningmodel for a particular machine learning algorithm. The machine learningsystem may then test this machine learning model using the test set 225to generate a performance score, such as a mean squared error (e.g., forregression), a mean absolute error (e.g., for regression), an area underreceiver operating characteristic curve (e.g., for classification),and/or the like. If the machine learning model performs adequately(e.g., with a performance score that satisfies a threshold), then themachine learning system may store that machine learning model as atrained machine learning model 245 to be used to analyze newobservations, as described below in connection with FIG. 3.

In some implementations, the machine learning system may performcross-validation, as described above, for multiple machine learningalgorithms (e.g., independently), such as a regularized regressionalgorithm, different types of regularized regression algorithms, adecision tree algorithm, different types of decision tree algorithms,and/or the like. Based on performing cross-validation for multiplemachine learning algorithms, the machine learning system may generatemultiple machine learning models, where each machine learning model hasthe best overall cross-validation score for a corresponding machinelearning algorithm. The machine learning system may then train eachmachine learning model using the entire training set 220 (e.g., withoutcross-validation), and may test each machine learning model using thetest set 225 to generate a corresponding performance score for eachmachine learning model. The machine learning model may compare theperformance scores for each machine learning model, and may select themachine learning model with the best (e.g., highest accuracy, lowesterror, closest to a desired threshold, and/or the like) performancescore as the trained machine learning model 245.

As indicated above, FIG. 2 is provided as an example. Other examples maydiffer from what is described in connection with FIG. 2. For example,the machine learning model may be trained using a different process thanwhat is described in connection with FIG. 2. Additionally, oralternatively, the machine learning model may employ a different machinelearning algorithm than what is described in connection with FIG. 2,such as a Bayesian estimation algorithm, a k-nearest neighbor algorithm,an a priori algorithm, a k-means algorithm, a support vector machinealgorithm, a neural network algorithm (e.g., a convolutional neuralnetwork algorithm), a deep learning algorithm, and/or the like.

FIG. 3 is a diagram illustrating an example 300 of applying a trainedmachine learning model to a new observation. The new observation may beinput to a machine learning system that stores a trained machinelearning model 305. In some implementations, the trained machinelearning model 305 may be the trained machine learning model 245described above in connection with FIG. 2. The machine learning systemmay include a computing device, a server, a cloud computing environment,and/or the like, such as the processing platform 810.

As shown by reference number 310, the machine learning system mayreceive a new observation (or a set of new observations), and may inputthe new observation to the machine learning model 305. As shown, the newobservation may include a first feature of event, a second feature oftheme, a third feature of type of transaction parameter, and so on, asan example. The machine learning system may apply the trained machinelearning model 305 to the new observation to generate an output (e.g., aresult). The type of output may depend on the type of machine learningmodel and/or the type of machine learning task being performed. Forexample, the output may include a predicted (e.g., estimated) value oftarget variable (e.g., a value within a continuous range of values, adiscrete value, a label, a class, a classification, and/or the like),such as when supervised learning is employed. Additionally, oralternatively, the output may include information that identifies acluster to which the new observation belongs, information that indicatesa degree of similarity between the new observations and one or moreprior observations (e.g., which may have previously been newobservations input to the machine learning model and/or observationsused to train the machine learning model), and/or the like, such as whenunsupervised learning is employed.

In some implementations, the trained machine learning model 305 maypredict an entity related to an entity associated with the newobservation and a type of relationship for the target variable ofrelated entity for the new observation, as shown by reference number315. Based on this prediction (e.g., based on the value having aparticular label/classification, based on the value satisfying orfailing to satisfy a threshold, and/or the like), the machine learningsystem may provide a recommendation, such as Entity 8 and Entity 9 are aset of related entities and the type of relationship is a parent-childrelationship. Additionally, or alternatively, the machine learningsystem may perform an automated action and/or may cause an automatedaction to be performed (e.g., by instructing another device to performthe automated action), such as storing information (e.g., in a datastructure stored in a memory associated with the server device 805and/or the processing platform 810) identifying Entity 8 and Entity 9 asa set of related entities, storing information identifying a type ofrelationship as a parent-child relationship, modifying entityinformation associated with Entity 8 and/or Entity 9 to indicate thatEntity 8 and Entity 9 are included in a set of related entities, and/oranother automated action associated with identifying Entity 8 and Entity9 as a set of related entities.

In some implementations, the trained machine learning model 305 mayclassify (e.g. cluster) the new observation in a cluster, as shown byreference number 320. The observations within a cluster may have athreshold degree of similarity. For example, the observations within acluster may be observations indicating that a set of entities arerelated. Based on classifying the new observation in the cluster, themachine learning system may provide a recommendation, such as Entity 8is related to Entity 9, Entity 8 and Entity 9 form a set of relatedentities, a parent-child relationship is associated with Entity 8 andEntity 9, Entity 8 is a parent of Entity 9, and/or anotherrecommendation associated with determining that Entity 8 and Entity 9are included in a set of related entities. Additionally, oralternatively, the machine learning system may perform an automatedaction and/or may cause an automated action to be performed (e.g., byinstructing another device to perform the automated action), such asstoring information (e.g., in a data structure stored in a memoryassociated with the server device 805 and/or the processing platform810) identifying Entity 8 and Entity 9 as a set of related entities,storing information identifying a type of relationship as a parent-childrelationship, modifying entity information associated with Entity 8and/or Entity 9 to indicate that Entity 8 and Entity 9 are included in aset of related entities, and/or another automated action associated withidentifying Entity 8 and Entity 9 as a set of related entities.

In this way, the machine learning system may apply a rigorous andautomated process to determine sets of related entities. The machinelearning system enables recognition and/or identification of tens,hundreds, thousands, or millions of features and/or feature values fortens, hundreds, thousands, or millions of observations, therebyincreasing an accuracy and consistency of determining sets of relatedentities relative to requiring computing resources to be allocated fortens, hundreds, or thousands of operators to manually determine sets ofrelated entities using the features or feature values.

As indicated above, FIG. 3 is provided as an example. Other examples maydiffer from what is described in connection with FIG. 3.

FIG. 4 is a diagram illustrating an example 400 of training a machinelearning model. The machine learning model training described herein maybe performed using a machine learning system. The machine learningsystem may include a computing device, a server, a cloud computingenvironment, and/or the like, such as processing platform 810.

As shown by reference number 405, a machine learning model may betrained using a set of observations. The set of observations may beobtained and/or input from historical data, such as data gathered duringone or more processes described herein. For example, the set ofobservations may include data gathered from the server device 805 and/orthe processing platform 810, as described elsewhere herein. In someimplementations, the machine learning system may receive the set ofobservations (e.g., as input) from the server device 805 and/or theprocessing platform 810.

As shown by reference number 410, a feature set may be derived from theset of observations. In some implementations, the machine learningsystem may determine variable values for a specific observation based oninput received from the server device 805 and/or the processing platform810. For example, the machine learning system may identify a feature setin a manner similar to that described above with respect to FIG. 2.

As an example, a feature set for a set of observations may include afirst feature of net worth, a second feature of type of age, a thirdfeature of type of relationship, and so on. As shown, for a firstobservation, the first feature may have a value of $150,300, the secondfeature may have a value of 22, the third feature may have a value ofmarried, and so on. These features and feature values are provided asexamples, and may differ in other examples. For example, the feature setmay include one or more of the following features: an accountcharacteristic (e.g., an interest rate, a minimum balance to bemaintained, a fee, a rewards program, an outstanding balance, a creditlimit, and/or another account characteristic), a length of relationship,a type of account, a socioeconomic factor (e.g., income, education,occupation, net worth, credit score, and/or another socioeconomicfactor) and/or another feature suitable for determining a recommendedset of accounts for a set of related entities. In some implementations,the machine learning system may pre-process and/or performdimensionality reduction to reduce the feature set and/or combinefeatures of the feature set to a minimum feature set. A machine learningmodel may be trained on the minimum feature set, thereby conservingresources of the machine learning system (e.g., processing resources,memory resources, and/or the like) used to train the machine learningmodel.

As shown by reference number 415, the set of observations may beassociated with a target variable type. The target variable mayrepresent a value that a machine learning model is being trained topredict, and the feature set may represent the variables that are inputto a trained machine learning model to predict a value for the targetvariable. The set of observations may include target variable values sothat the machine learning model can be trained to recognize patterns inthe feature set that lead to a target variable value.

In some implementations, the machine learning model may be trained on aset of observations that do not include a target variable (or thatinclude a target variable, but the machine learning model is not beingexecuted to predict the target variable). This may be referred to as anunsupervised learning model, an automated data analysis model, anautomated signal extraction model, and/or the like. In this case, themachine learning model may learn patterns from the set of observationswithout labeling or supervision, and may provide output that indicatessuch patterns, such as by using clustering and/or association toidentify related groups of items within the set of observations.

As further shown, the machine learning system may partition the set ofobservations into a training set 420 that includes a first subset ofobservations, of the set of observations, and a test set 425 thatincludes a second subset of observations of the set of observations. Thetraining set 420 may be used to train (e.g., fit, tune, and/or the like)the machine learning model, while the test set 425 may be used toevaluate a machine learning model that is trained using the training set420, for example, in a manner similar to that described above withrespect to FIG. 2.

As shown by reference number 430, the machine learning system may traina machine learning model using the training set 420. For example, themachine learning system may train the machine model using the trainingset 420 in a manner similar to that described above with respect to FIG.2.

As shown by reference number 435, the machine learning system may useone or more hyperparameter sets 440 to tune the machine learning model.For example, the machine learning system may use one or morehyperparameter sets 440 to tune the machine learning model in a mannersimilar to that described above with respect to claim 2.

In some implementations, the machine learning system may performcross-validation when training a machine learning model. For example,the machine learning system may perform cross-validation in a mannersimilar to that described above with respect to FIG. 2.

In some implementations, the machine learning system may then test themachine learning model using the test set 425 to generate a performancescore, such as a mean squared error (e.g., for regression), a meanabsolute error (e.g., for regression), an area under receiver operatingcharacteristic curve (e.g., for classification), and/or the like. Forexample, the machine learning system may test the machine learning modelin a manner similar to that described above with respect to FIG. 2. Ifthe machine learning model performs adequately (e.g., with a performancescore that satisfies a threshold), then the machine learning system maystore that machine learning model as a trained machine learning model445 to be used to analyze new observations, as described below inconnection with FIG. 5.

As indicated above, FIG. 4 is provided as an example. Other examples maydiffer from what is described in connection with FIG. 4. For example,the machine learning model may be trained using a different process thanwhat is described in connection with FIG. 4. Additionally, oralternatively, the machine learning model may employ a different machinelearning algorithm than what is described in connection with FIG. 4,such as a Bayesian estimation algorithm, a k-nearest neighbor algorithm,an a priori algorithm, a k-means algorithm, a support vector machinealgorithm, a neural network algorithm (e.g., a convolutional neuralnetwork algorithm), a deep learning algorithm, and/or the like.

FIG. 5 is a diagram illustrating an example 500 of applying a trainedmachine learning model to a new observation. The new observation may beinput to a machine learning system that stores a trained machinelearning model 505. In some implementations, the trained machinelearning model 505 may be the trained machine learning model 445described above in connection with FIG. 4. The machine learning systemmay include a computing device, a server, a cloud computing environment,and/or the like, such as processing platform 810.

As shown by reference number 510, the machine learning system mayreceive a new observation (or a set of new observations), and may inputthe new observation to the machine learning model 505. As shown, the newobservation may include a first feature of net worth, a second featureof age, a third feature of type of relationship, and so on, as anexample. The machine learning system may apply the trained machinelearning model 505 to the new observation to generate an output (e.g., aresult). For example, the machine learning system may apply the trainedmachine learning model 505 to the new observation to generate an outputin a manner similar to that described above with respect to FIG. 3.

In some implementations, the trained machine learning model 505 maypredict a value of Account Set 3 for the target variable of set ofaccounts for the new observation, as shown by reference number 515.Based on this prediction (e.g., based on the value having a particularlabel/classification, based on the value satisfying or failing tosatisfy a threshold, and/or the like), the machine learning system mayprovide a recommendation, such one or more modifications to be made to aset of accounts associated with a set of related entities. Additionally,or alternatively, the machine learning system may perform an automatedaction and/or may cause an automated action to be performed (e.g., byinstructing another device to perform the automated action), such asautomatically providing a recommendation to open, close, and/or modifyone or more accounts associated with a set of related entities;automatically opening, closing, and/or modifying one or more accountsassociated with a set of related entities, automatically providing alink for establishing a telephone to an account provider to enable anentity to open, close, and/or modify an account, and/or anotherautomated action associated with modifying one or more accountsassociated with a set of related entities. In some implementations, therecommendation and/or the automated action may be based on the targetvariable value having a particular label (e.g., classification,categorization, and/or the like), may be based on whether the targetvariable value satisfies one or more threshold (e.g., whether the targetvariable value is greater than a threshold, is less than a threshold, isequal to a threshold, falls within a range of threshold values, and/orthe like), and/or the like.

In some implementations, the trained machine learning model 505 mayclassify (e.g., cluster) the new observation in a cluster, as shown byreference number 520. The observations within a cluster may have athreshold degree of similarity. Based on classifying the new observationin the cluster, the machine learning system may provide arecommendation, such as one or more modifications to make to a set ofaccounts associated with a set of related entities. Additionally, oralternatively, the machine learning system may perform an automatedaction and/or may cause an automated action to be performed (e.g., byinstructing another device to perform the automated action), such asautomatically modifying one or more accounts associated with the set ofrelated entities.

In this way, the machine learning system may apply a rigorous andautomated process to determining one or more modifications to a set ofaccounts associated with a set of related entities. The machine learningsystem enables recognition and/or identification of tens, hundreds,thousands, or millions of features and/or feature values for tens,hundreds, thousands, or millions of observations, thereby increasing anaccuracy and consistency of determining one or more modifications to aset of accounts associated with a set of related entities relative torequiring computing resources to be allocated for tens, hundreds, orthousands of operators to manually determining one or more modificationsto a set of accounts associated with a set of related entities using thefeatures or feature values.

As indicated above, FIG. 5 is provided as an example. Other examples maydiffer from what is described in connection with FIG. 5.

FIG. 6 is a diagram illustrating an example 600 of training a machinelearning model. The machine learning model training described herein maybe performed using a machine learning system. The machine learningsystem may include a computing device, a server, a cloud computingenvironment, and/or the like, such as the processing platform 810.

As shown by reference number 605, a machine learning model may betrained using a set of observations. The set of observations may beobtained and/or input from historical data, such as data gathered duringone or more processes described herein. For example, the set ofobservations may include data gathered from the server device 805 and/orthe processing platform 810, as described elsewhere herein. In someimplementations, the machine learning system may receive the set ofobservations (e.g., as input) from the server device 805 and/or theprocessing platform 810.

As shown by reference number 610, a feature set may be derived from theset of observations. The feature set may include a set of variabletypes. A variable type may be referred to as a feature. A specificobservation may include a set of variable values corresponding to theset of variable types. A set of variable values may be specific to anobservation. In some cases, different observations may be associatedwith different sets of variable values, sometimes referred to as featurevalues. In some implementations, the machine learning system maydetermine variable values for a specific observation based on inputreceived from the server device 805 and/or the processing platform 810.For example, the machine learning system may determine variable valuesfor a specific observation in a manner similar to that described abovewith respect to FIG. 2.

As an example, a feature set for a set of observations may include afirst feature of item purchased, a second feature of type ofrelationship, a third feature of commonality factor, and so on. Asshown, for a first observation, the first feature may have a value ofdiapers, the second feature may have a value of married, the thirdfeature may have a value of 97, and so on. These features and featurevalues are provided as examples, and may differ in other examples. Forexample, the feature set may include one or more of the followingfeatures: a socioeconomic factor, a length of a relationship, a date ofa purchase and/or sale, and/or another feature suitable for determiningan occurrence of a life event. In some implementations, the machinelearning system may pre-process and/or perform dimensionality reductionto reduce the feature set and/or combine features of the feature set toa minimum feature set. A machine learning model may be trained on theminimum feature set, thereby conserving resources of the machinelearning system (e.g., processing resources, memory resources, and/orthe like) used to train the machine learning model.

As shown by reference number 615, the set of observations may beassociated with a target variable type. The target variable mayrepresent a value that a machine learning model is being trained topredict, and the feature set may represent the variables that are inputto a trained machine learning model to predict a value for the targetvariable. The set of observations may include target variable values sothat the machine learning model can be trained to recognize patterns inthe feature set that lead to a target variable value.

In some implementations, the machine learning model may be trained on aset of observations that do not include a target variable (or thatinclude a target variable, but the machine learning model is not beingexecuted to predict the target variable). This may be referred to as anunsupervised learning model, an automated data analysis model, anautomated signal extraction model, and/or the like. In this case, themachine learning model may learn patterns from the set of observationswithout labeling or supervision, and may provide output that indicatessuch patterns, such as by using clustering and/or association toidentify related groups of items within the set of observations.

As further shown, the machine learning system may partition the set ofobservations into a training set 620 that includes a first subset ofobservations, of the set of observations, and a test set 625 thatincludes a second subset of observations of the set of observations. Thetraining set 620 may be used to train (e.g., fit, tune, and/or the like)the machine learning model, while the test set 625 may be used toevaluate a machine learning model that is trained using the training set620, for example, in a manner similar to that described above withrespect to FIG. 2.

As shown by reference number 630, the machine learning system may traina machine learning model using the training set 620. For example, themachine learning system may train the machine model using the trainingset 620 in a manner similar to that described above with respect to FIG.2.

As shown by reference number 635, the machine learning system may useone or more hyperparameter sets 640 to tune the machine learning model.For example, the machine learning system may use one or morehyperparameter sets 640 to tune the machine learning model in a mannersimilar to that described above with respect to claim 2.

In some implementations, the machine learning system may performcross-validation when training a machine learning model. For example,the machine learning system may perform cross-validation in a mannersimilar to that described above with respect to FIG. 2.

In some implementations, the machine learning system may then test thismachine learning model using the test set 625 to generate a performancescore, such as a mean squared error (e.g., for regression), a meanabsolute error (e.g., for regression), an area under receiver operatingcharacteristic curve (e.g., for classification), and/or the like. Forexample, the machine learning system may test the machine learning modelin a manner similar to that described above with respect to FIG. 2. Ifthe machine learning model performs adequately (e.g., with a performancescore that satisfies a threshold), then the machine learning system maystore that machine learning model as a trained machine learning model645 to be used to analyze new observations, as described below inconnection with FIG. 7.

As indicated above, FIG. 6 is provided as an example. Other examples maydiffer from what is described in connection with FIG. 6. For example,the machine learning model may be trained using a different process thanwhat is described in connection with FIG. 6. Additionally, oralternatively, the machine learning model may employ a different machinelearning algorithm than what is described in connection with FIG. 6,such as a Bayesian estimation algorithm, a k-nearest neighbor algorithm,an a priori algorithm, a k-means algorithm, a support vector machinealgorithm, a neural network algorithm (e.g., a convolutional neuralnetwork algorithm), a deep learning algorithm, and/or the like.

FIG. 7 is a diagram illustrating an example 700 of applying a trainedmachine learning model to a new observation. The new observation may beinput to a machine learning system that stores a trained machinelearning model 705. In some implementations, the trained machinelearning model 705 may be the trained machine learning model 645described above in connection with FIG. 6. The machine learning systemmay include a computing device, a server, a cloud computing environment,and/or the like, such as processing platform 810.

As shown by reference number 710, the machine learning system mayreceive a new observation (or a set of new observations), and may inputthe new observation to the machine learning model 705. As shown, the newobservation may include a first feature of item purchased, a secondfeature of type of relationship, a third feature of commonality factor,and so on, as an example. The machine learning system may apply thetrained machine learning model 705 to the new observation to generate anoutput (e.g., a result). The type of output may depend on the type ofmachine learning model and/or the type of machine learning task beingperformed. For example, the output may include a predicted (e.g.,estimated) value of target variable (e.g., a value within a continuousrange of values, a discrete value, a label, a class, a classification,and/or the like), such as when supervised learning is employed.Additionally, or alternatively, the output may include information thatidentifies a cluster to which the new observation belongs, informationthat indicates a degree of similarity between the new observation andone or more prior observations (e.g., which may have previously been newobservations input to the machine learning model and/or observationsused to train the machine learning model), and/or the like, such as whenunsupervised learning is employed.

In some implementations, the trained machine learning model 705 maypredict a value of death of a family member for the target variable oflife event for the new observation, as shown by reference number 715.Based on this prediction (e.g., based on the value having a particularlabel/classification, based on the value satisfying or failing tosatisfy a threshold, and/or the like), the machine learning system mayprovide a recommendation, such as providing information identifying aset of related entities and indicating that a particular life event hasoccurred. Additionally, or alternatively, the machine learning systemmay perform an automated action and/or may cause an automated action tobe performed (e.g., by instructing another device to perform theautomated action), such as providing information identifying a set ofrelated entities and indicating that a particular life event hasoccurred to the account optimization model to be used to determine oneor more modifications to the set of accounts associated with theidentified set of related entities. In some implementations, therecommendation and/or the automated action may be based on the targetvariable value having a particular label (e.g., classification,categorization, and/or the like), may be based on whether the targetvariable value satisfies one or more threshold (e.g., whether the targetvariable value is greater than a threshold, is less than a threshold, isequal to a threshold, falls within a range of threshold values, and/orthe like), and/or the like.

In some implementations, the trained machine learning model 705 mayclassify (e.g., cluster) the new observation in a cluster, as shown byreference number 720. The observations within a cluster may have athreshold degree of similarity. Based on classifying the new observationin the cluster, the machine learning system may provide informationidentifying a set of life events associated with sets of relatedentities associated.

In this way, the machine learning system may apply a rigorous andautomated process to determining an occurrence of a life event. Themachine learning system enables recognition and/or identification oftens, hundreds, thousands, or millions of features and/or feature valuesfor tens, hundreds, thousands, or millions of observations, therebyincreasing an accuracy and consistency of determining an occurrence of alife event relative to requiring computing resources to be allocated fortens, hundreds, or thousands of operators to manually determining anoccurrence of a life event using the features or feature values.

As indicated above, FIG. 7 is provided as an example. Other examples maydiffer from what is described in connection with FIG. 7.

FIG. 8 is a diagram of an example environment 800 in which systemsand/or methods, described herein, may be implemented. As shown in FIG.8, environment 800 may include a server device 805, a processingplatform 810, and a network 825. Devices of environment 800 mayinterconnect via wired connections, wireless connections, or acombination of wired and wireless connections.

Server device 805 includes one or more devices capable of storing,processing, and/or routing information associated with transactionsrelated to an entity and information associated with an entity. In someimplementations, server device 805 may provide transaction informationand/or entity information to processing platform 810. In someimplementations, server device 805 may include a communication interfacethat allows server device 805 to receive information from and/ortransmit information to other devices in environment 800.

Processing platform 810 includes one or more computing resourcesassigned to analysis of entity information and/or transactioninformation to determine related sets of entities, sets of accountsassociated with related sets of entities, modifications to sets ofaccounts associated with related sets of entities, and/or an occurrenceof a life event. For example, processing platform 810 may be a platformimplemented by cloud computing environment 820 that may analyze entityinformation and/or transaction information to determine related sets ofentities, sets of accounts associated with related sets of entities,modifications to sets of accounts associated with related sets ofentities, and/or an occurrence of a life event. In some implementations,processing platform 810 is implemented by computing resources 815 ofcloud computing environment 820.

Processing platform 810 may include a server device or a group of serverdevices. In some implementations, processing platform 810 may be hostedin cloud computing environment 820. Notably, while implementationsdescribed herein may describe processing platform 810 as being hosted incloud computing environment 820, in some implementations, processingplatform 810 may be non-cloud-based or may be partially cloud-based.

Cloud computing environment 820 includes an environment that deliverscomputing as a service, whereby shared resources, services, and/or thelike may be provided to server device 805 and/or processing device 810.Cloud computing environment 820 may provide computation, software, dataaccess, storage, and/or other services that do not require end-userknowledge of a physical location and configuration of a system and/or adevice that delivers the services. As shown, cloud computing environment820 may include processing device 810.

Computing resource 815 includes one or more personal computers,workstation computers, server devices, or another type of computationand/or communication device. In some implementations, computing resource815 may host processing platform 810. The cloud resources may includecompute instances executing in computing resource 815, storage devicesprovided in computing resource 815, data transfer devices provided bycomputing resource 815, and/or the like. In some implementations,computing resource 815 may communicate with other computing resources815 via wired connections, wireless connections, or a combination ofwired and wireless connections.

As further shown in FIG. 8, computing resource 815 may include a groupof cloud resources, such as one or more applications (“APPs”) 815-1, oneor more virtual machines (“VMs”) 815-2, virtualized storage (“VSs”)815-3, one or more hypervisors (“HYPs”) 815-4, or the like.

Application 815-1 includes one or more software applications that may beprovided to or accessed by processing platform 810. Application 815-1may eliminate a need to install and execute the software applications onprocessing platform 810. For example, application 815-1 may includesoftware associated with processing platform 810 and/or any othersoftware capable of being provided via cloud computing environment 820.In some implementations, one application 815-1 may send/receiveinformation to/from one or more other applications 815-1, via virtualmachine 815-2.

Virtual machine 815-2 includes a software implementation of a machine(e.g., a computer) that executes programs like a physical machine.Virtual machine 815-2 may be either a system virtual machine or aprocess virtual machine, depending upon use and degree of correspondenceto any real machine by virtual machine 815-2. A system virtual machinemay provide a complete system platform that supports execution of acomplete operating system (“OS”). A process virtual machine may executea single program and may support a single process. In someimplementations, virtual machine 815-2 may execute on behalf of a user(e.g., processing platform 810), and may manage infrastructure of cloudcomputing environment 820, such as data management, synchronization, orlong-duration data transfers.

Virtualized storage 815-3 includes one or more storage systems and/orone or more devices that use virtualization techniques within thestorage systems or devices of computing resource 815. In someimplementations, within the context of a storage system, types ofvirtualizations may include block virtualization and filevirtualization. Block virtualization may refer to abstraction (orseparation) of logical storage from physical storage so that the storagesystem may be accessed without regard to physical storage orheterogeneous structure. The separation may permit administrators of thestorage system flexibility in how the administrators manage storage forend users. File virtualization may eliminate dependencies between dataaccessed at a file level and a location where files are physicallystored. This may enable optimization of storage use, serverconsolidation, and/or performance of non-disruptive file migrations.

Hypervisor 815-4 provides hardware virtualization techniques that allowmultiple operating systems (e.g., “guest operating systems”) to executeconcurrently on a host computer, such as computing resource 815.Hypervisor 815-4 may present a virtual operating platform to the “guestoperating systems” and may manage the execution of the guest operatingsystems. Multiple instances of a variety of operating systems may sharevirtualized hardware resources.

Network 825 includes one or more wired and/or wireless networks. Forexample, network 825 can include a cellular network (e. g., a long-termevolution (LTE) network, a code division multiple access (CDMA) network,a 3G network, a 4G network, a 5G network, another type of nextgeneration network, and/or the like), a public land mobile network(PLMN), a local area network (LAN), a wide area network (WAN), ametropolitan area network (MAN), a telephone network (e.g., the PublicSwitched Telephone Network (PSTN)), a private network, an ad hocnetwork, an intranet, the Internet, a fiber optic-based network, a cloudcomputing network, a core network, and/or the like, and/or a combinationof these or other types of networks.

The number and arrangement of devices and networks shown in FIG. 8 areprovided as one or more examples. In practice, there may be additionaldevices and/or networks, fewer devices and/or networks, differentdevices and/or networks, or differently arranged devices and/or networksthan those shown in FIG. 8. Furthermore, two or more devices shown inFIG. 8 may be implemented within a single device, or a single deviceshown in FIG. 8 may be implemented as multiple, distributed devices.Additionally, or alternatively, a set of devices (e.g., one or moredevices) of environment 800 may perform one or more functions describedas being performed by another set of devices of environment 800.

FIG. 9 is a diagram of example components of a device 900. Device 900may correspond to server device 805, processing platform 810, and/orcomputing resource 815. In some implementations, server device 805,processing platform 810, and/or computing resource 815 may include oneor more devices 900 and/or one or more components of device 900. Asshown in FIG. 9, device 900 may include a bus 910, a processor 920, amemory 930, a storage component 940, an input component 950, an outputcomponent 960, and a communication interface 970.

Bus 910 includes a component that permits communication among multiplecomponents of device 900. Processor 920 is implemented in hardware,firmware, and/or a combination of hardware and software. Processor 920is a central processing unit (CPU), a graphics processing unit (GPU), anaccelerated processing unit (APU), a microprocessor, a microcontroller,a digital signal processor (DSP), a field-programmable gate array(FPGA), an application-specific integrated circuit (ASIC), or anothertype of processing component. In some implementations, processor 920includes one or more processors capable of being programmed to perform afunction. Memory 930 includes a random access memory (RAM), a read onlymemory (ROM), and/or another type of dynamic or static storage device(e.g., a flash memory, a magnetic memory, and/or an optical memory) thatstores information and/or instructions for use by processor 920.

Storage component 940 stores information and/or software related to theoperation and use of device 900. For example, storage component 940 mayinclude a hard disk (e.g., a magnetic disk, an optical disk, and/or amagneto-optic disk), a solid state drive (SSD), a compact disc (CD), adigital versatile disc (DVD), a floppy disk, a cartridge, a magnetictape, and/or another type of non-transitory computer-readable medium,along with a corresponding drive.

Input component 950 includes a component that permits device 900 toreceive information, such as via user input (e.g., a touch screendisplay, a keyboard, a keypad, a mouse, a button, a switch, and/or amicrophone). Additionally, or alternatively, input component 950 mayinclude a component for determining location (e.g., a global positioningsystem (GPS) component) and/or a sensor (e.g., an accelerometer, agyroscope, an actuator, another type of positional or environmentalsensor, and/or the like). Output component 960 includes a component thatprovides output information from device 900 (via, e.g., a display, aspeaker, a haptic feedback component, an audio or visual indicator,and/or the like).

Communication interface 970 includes a transceiver-like component (e.g.,a transceiver, a separate receiver, a separate transmitter, and/or thelike) that enables device 900 to communicate with other devices, such asvia a wired connection, a wireless connection, or a combination of wiredand wireless connections. Communication interface 970 may permit device900 to receive information from another device and/or provideinformation to another device. For example, communication interface 970may include an Ethernet interface, an optical interface, a coaxialinterface, an infrared interface, a radio frequency (RF) interface, auniversal serial bus (USB) interface, a Wi-Fi interface, a cellularnetwork interface, and/or the like.

Device 900 may perform one or more processes described herein. Device900 may perform these processes based on processor 920 executingsoftware instructions stored by a non-transitory computer-readablemedium, such as memory 930 and/or storage component 940. As used herein,the term “computer-readable medium” refers to a non-transitory memorydevice. A memory device includes memory space within a single physicalstorage device or memory space spread across multiple physical storagedevices.

Software instructions may be read into memory 930 and/or storagecomponent 940 from another computer-readable medium or from anotherdevice via communication interface 970. When executed, softwareinstructions stored in memory 930 and/or storage component 940 may causeprocessor 920 to perform one or more processes described herein.Additionally, or alternatively, hardware circuitry may be used in placeof or in combination with software instructions to perform one or moreprocesses described herein. Thus, implementations described herein arenot limited to any specific combination of hardware circuitry andsoftware.

The number and arrangement of components shown in FIG. 9 are provided asan example. In practice, device 900 may include additional components,fewer components, different components, or differently arrangedcomponents than those shown in FIG. 9. Additionally, or alternatively, aset of components (e.g., one or more components) of device 900 mayperform one or more functions described as being performed by anotherset of components of device 900.

FIG. 10 10 is a flow chart of an example process 1000 for performing amerger and optimization operation. In some implementations, one or moreprocess blocks of FIG. 10 may be performed by a device (e.g., processingplatform 810). In some implementations, one or more process blocks ofFIG. 10 may be performed by another device or a group of devicesseparate from or including the device, such as server device 805,computing resource 815, and/or the like.

As shown in FIG. 10, process 1000 may include receiving transactioninformation for a plurality of entities, wherein the transactioninformation identifies a plurality of transactions associated with theplurality of entities (block 1010). For example, the processing platform810 (e.g., using processor 920, memory 930, storage component 940, inputcomponent 950, output component 960, communication interface 970, and/orthe like) may receive transaction information for a plurality ofentities, as described above. In some implementations, the transactioninformation identifies a plurality of transactions associated with theplurality of entities.

As further shown in FIG. 10, process 1000 may include receiving entityinformation associated with the plurality of entities (block 1020). Forexample, the processing platform 810 (e.g., using processor 920, memory930, storage component 940, input component 950, output component 960,communication interface 970, and/or the like) may receive entityinformation associated with the plurality of entities, as describedabove.

As further shown in FIG. 10, process 1000 may include performing atraining operation when generating a first model by portioning thetransaction information, the entity information, and informationidentifying an event, a theme, or a transaction parameter associatedwith a plurality of types of relationships into a training set, avalidation set, and a test set, wherein performing the trainingoperation comprises: using the training set to fit the first model,using the validation set to provide an evaluation of a fit of the firstmodel on the training set while tuning the first model, and using thetest set to provide an evaluation of the first model on the training set(block 1030). For example, the processing platform 810 (e.g., usingprocessor 920, memory 930, storage component 940, input component 950,output component 960, communication interface 970, and/or the like) mayperform a training operation when generating a first model by portioningthe transaction information, the entity information, and informationidentifying an event, a theme, or a transaction parameter associatedwith a plurality of types of relationships into a training set, avalidation set, and a test set, as described above.

In some implementations, performing the training operation comprisesusing the training set to fit the first model, using the validation setto provide an evaluation of a fit of the first model on the training setwhile tuning the first model, and using the test set to provide anevaluation of the first model on the training set.

As further shown in FIG. 10, process 1000 may include processing, usingthe first model, the transaction information and the entity informationto identify a set of related entities and a type of relationshipassociated with the set of related entities, wherein the set of relatedentities is a subset of the plurality of entities, wherein the set ofrelated entities includes a first entity and a second entity, whereinthe first model receives, as inputs, the transaction information and theentity information, and wherein the first model outputs informationidentifying the set of related entities and the type of relationshipassociated with the set of related entities based on the set of relatedentities being associated with the event, the theme, or the transactionparameter (block 1040). For example, the processing platform 810 (e.g.,using processor 920, memory 930, storage component 940, input component950, output component 960, communication interface 970, and/or the like)may process, using the first model, the transaction information and theentity information to identify a set of related entities and a type ofrelationship associated with the set of related entities, as describedabove.

In some implementations, the set of related entities is a subset of theplurality of entities. In some implementations, the set of relatedentities includes a first entity and a second entity. In someimplementations, the first model receives, as inputs, the transactioninformation and the entity information. In some implementations, thefirst model outputs information identifying the set of related entitiesand the type of relationship associated with the set of related entitiesbased on the set of related entities being associated with the event,the theme, or the transaction parameter.

As further shown in FIG. 10, process 1000 may include determining, usinga second model, one or more first modifications to a first set ofaccounts associated with the first entity and one or more secondmodifications to a second set of accounts associated with the secondentity based on the type of relationship associated with the set ofrelated entities, wherein the second model receives informationidentifying the first set of accounts, information identifying thesecond set of accounts, and information identifying the type ofrelationship, and wherein the second model outputs informationidentifying the one or more first modifications and the one or moresecond modifications (block 1050). For example, the processing platform810 (e.g., using processor 920, memory 930, storage component 940, inputcomponent 950, output component 960, communication interface 970, and/orthe like) may determine, using a second model, one or more firstmodifications to a first set of accounts associated with the firstentity and one or more second modifications to a second set of accountsassociated with the second entity based on the type of relationshipassociated with the set of related entities, as described above. In someimplementations, the second model receives information identifying thefirst set of accounts, information identifying the second set ofaccounts, and information identifying the type of relationship. In someimplementations, the second model outputs information identifying theone or more first modifications and the one or more secondmodifications.

As further shown in FIG. 10, process 1000 may include performing one ormore actions based on at least one of the determined one or more firstmodifications or the determined one or more second modifications (block1060). For example, the processing platform 810 (e.g., using processor920, memory 930, storage component 940, input component 950, outputcomponent 960, communication interface 970, and/or the like) may performone or more actions based on at least one of the determined one or morefirst modifications or the determined one or more second modifications,as described above.

Process 1000 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or in connection with one or more other processes describedelsewhere herein.

In a first implementation, process 1000 includes performing anothertraining operation when generating the second model by portioning aportion of the transaction information that is associated with the typeof relationship, a portion of the entity information that is associatedwith the type of relationship, and a portion of the informationidentifying the event, the theme, or the transaction parameter that isassociated with the type of relationship into another training set,another validation set, and another test set, wherein performing theother training operation comprises: using the other training set to fitthe second model, using the other validation set to provide anevaluation of a fit of the second model on the other training set whiletuning the second model, and using the other test set to provide anevaluation of the second model on the other training set.

For example, the processing platform 810 may perform another trainingoperation when generating the second model by portioning a portion ofthe transaction information that is associated with the type ofrelationship, a portion of the entity information that is associatedwith the type of relationship, and a portion of the informationidentifying the event, the theme, or the transaction parameter that isassociated with the type of relationship into another training set,another validation set, and another test set, as described above. Whenperforming the other training operation, the processing platform may usethe other training set to fit the second model, use the other validationset to provide an evaluation of a fit of the second model on the othertraining set while tuning the second model, and use the other test setto provide an evaluation of the second model on the other training set,as described above.

In a second implementation, alone or in combination with the firstimplementation, processing the transaction information and the entityinformation to identify the set of related entities and the type ofrelationship associated with the set of related entities includes:utilizing machine learning to process the transaction information andthe entity information to identify the set of related entities and thetype of relationship associated with the set of related entities.

For example, when processing the transaction information and the entityinformation to identify the set of related entities and the type ofrelationship associated with the set of related entities, the processingplatform 810 may utilize machine learning to process the transactioninformation and the entity information to identify the set of relatedentities and the type of relationship associated with the set of relatedentities, as described above.

In a third implementation, alone or in combination with one or more ofthe first and second implementations, process 1000 includes obtainingtransaction information associated with the set of related entities;processing, using a third model, the transaction information associatedwith the set of related entities to determine an occurrence of theevent; and determining, using a fourth model, one or more thirdmodifications to the first set of accounts and one or more fourthmodifications to the second set of accounts based on the occurrence ofthe event. For example, the processing platform 810 may obtaintransaction information associated with the set of related entities;processing, may use a third model, the transaction informationassociated with the set of related entities to determine an occurrenceof the event; and may determine, using a fourth model, one or more thirdmodifications to the first set of accounts and one or more fourthmodifications to the second set of accounts based on the occurrence ofthe event, as described above.

In a fourth implementation, alone or in combination with one or more ofthe first through third implementations, the one or more actionscomprise: providing the information identifying the one or more firstmodifications and the information identifying the one or more secondmodifications, and the method further comprises: receiving an input fromthe first entity based on providing the information identifying the oneor more first modifications and the information identifying the one ormore second modifications; and modifying an account included in thefirst set of accounts to form a joint account associated with the firstentity and the second entity based on the input. For example, whenperforming the one or more actions, the processing platform 810 mayprovide the information identifying the one or more first modificationsand the information identifying the one or more second modifications, asdescribed above. The processing platform 810 may further receive aninput from the first entity based on providing the informationidentifying the one or more first modifications and the informationidentifying the one or more second modifications; and may modify anaccount included in the first set of accounts to form a joint accountassociated with the first entity and the second entity based on theinput, as described above.

In a fifth implementation, alone or in combination with one or more ofthe first through fourth implementations, the type of relationshipincludes a legal union between the first entity and the second entity.

In a sixth implementation, alone or in combination with one or more ofthe first through fifth implementations, the one or more actionscomprise at least one of: providing information indicating that thefirst account and the second account have been consolidated, providing arecommendation to form a joint account associated with the first entityand the second entity, automatically closing a first account included inthe first set of accounts, providing a recommendation to modify a secondaccount included in the second set of accounts, automatically modifyingthe second account, automatically providing one or more links forestablishing a telephone call to an account provider associated with thefirst account, providing a recommendation to open a new account, orautomatically opening the new account. For example, when performing theone or more actions, the processing platform 810 may perform at leastone of: indicating that the first account and the second account havebeen consolidated, providing a recommendation to form a joint accountassociated with the first entity and the second entity, automaticallyclosing a first account included in the first set of accounts, providinga recommendation to modify a second account included in the second setof accounts, automatically modifying the second account, automaticallyproviding one or more links for establishing a telephone call to anaccount provider associated with the first account, providing arecommendation to open a new account, or automatically opening the newaccount, as described above.

In a seventh implementation, alone or in combination with one or more ofthe first through sixth implementations, process 1000 includes obtainingtransaction information associated with the set of related entities,processing, using a third model, the transaction information associatedwith the set of related entities to determine an occurrence of anotherevent, and determining, using a fourth model, an additional modificationto the set of accounts based on the occurrence of the other event. Forexample, the processing platform 810 may obtain transaction informationassociated with the set of related entities, process, using a thirdmodel, the transaction information associated with the set of relatedentities to determine an occurrence of another event, and determine,using a fourth model, an additional modification to the set of accountsbased on the occurrence of the other event, as described herein.

Although FIG. 10 shows example blocks of process 1000, in someimplementations, process 1000 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 10. Additionally, or alternatively, two or more of theblocks of process 1000 may be performed in parallel.

The foregoing disclosure provides illustration and description, but isnot intended to be exhaustive or to limit the implementations to theprecise form disclosed. Modifications and variations may be made inlight of the above disclosure or may be acquired from practice of theimplementations.

As used herein, the term “component” is intended to be broadly construedas hardware, firmware, or a combination of hardware and software.

To the extent the implementations described herein collect, store,process, and/or utilize personal information provided by individuals, itshould be understood that such information is to be used in accordancewith all applicable laws. Additionally, the collection, storage,processing, and/or utilizing of such information may require consent ofthe individual to such activity. For example, collecting, storing,processing, and/or utilizing personal information or informationindicating a transaction history of an account may require an individualto consent via an opt-in procedure. Additionally, the individual may bepermitted to remove consent via an opt-out procedure. Storage and use ofpersonal information can be in an appropriately secure mannerappropriate for the type of information, for example, through variousencryption and anonymization techniques for particularly sensitiveinformation. Furthermore, to display and/or provide a link to a thirdparty website or program, an authorized individual associated with thethird party website or program may have to provide permission to displayand/or provide the link.

Some implementations are described herein in connection with thresholds.As used herein, satisfying a threshold may, depending on the context,refer to a value being greater than the threshold, more than thethreshold, higher than the threshold, greater than or equal to thethreshold, less than the threshold, fewer than the threshold, lower thanthe threshold, less than or equal to the threshold, equal to thethreshold, or the like.

It will be apparent that systems and/or methods described herein may beimplemented in different forms of hardware, firmware, or a combinationof hardware and software. The actual specialized control hardware orsoftware code used to implement these systems and/or methods is notlimiting of the implementations. Thus, the operation and behavior of thesystems and/or methods are described herein without reference tospecific software code—it being understood that software and hardwarecan be designed to implement the systems and/or methods based on thedescription herein.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of various implementations. In fact,many of these features may be combined in ways not specifically recitedin the claims and/or disclosed in the specification. Although eachdependent claim listed below may directly depend on only one claim, thedisclosure of various implementations includes each dependent claim incombination with every other claim in the claim set.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Further, asused herein, the article “the” is intended to include one or more itemsreferenced in connection with the article “the” and may be usedinterchangeably with “the one or more.” Furthermore, as used herein, theterm “set” is intended to include one or more items (e.g., relateditems, unrelated items, a combination of related and unrelated items,etc.), and may be used interchangeably with “one or more.” Where onlyone item is intended, the phrase “only one” or similar language is used.Also, as used herein, the terms “has,” “have,” “having,” or the like areintended to be open-ended terms. Further, the phrase “based on” isintended to mean “based, at least in part, on” unless explicitly statedotherwise. Also, as used herein, the term “or” is intended to beinclusive when used in a series and may be used interchangeably with“and/or,” unless explicitly stated otherwise (e.g., if used incombination with “either” or “only one of”).

What is claimed is:
 1. A method, comprising: performing, by a device, atraining operation to generate a first model by portioning transactioninformation, entity information, information identifying an event, atheme, or a transaction parameter associated with a plurality of typesof relationships into a training set, a validation set, and a test set;determining, by a device and using the first model, sets of relatedentities associated with a first entity, wherein the sets of relatedentities are associated with sets of accounts; identifying, by thedevice and using a second model, a type of account associated with eachaccount included in the sets of accounts associated with the sets ofrelated entities; determining, by the device and using the second model,a commonality factor associated with the type of account; determining,by the device and using the second model, to include the type of accountin a recommended set of accounts based on the commonality factor;determining, by the device and based on the recommended set of accounts,information identifying one or more modifications associated with thefirst entity; and performing, by the device and based on the one or moremodifications, one or more actions associated with the first entity. 2.The method of claim 1, wherein the one or more actions include:providing a recommendation to form an account associated with the typeof account, or opening the account, associated with the type of account,in association with the first entity.
 3. The method of claim 1, whereinthe type of account includes one or more of: a savings account, achecking account, or a brokerage account.
 4. The method of claim 1,wherein determining to include the type of account in the recommendedset of accounts comprises: comparing the commonality factor against athreshold commonality factor to determine to include the type of accountin the recommended set of accounts.
 5. The method of claim 1, whereinthe commonality factor is associated with a quantity or percentage ofthe sets of accounts having the type of account.
 6. The method of claim1, wherein the one or more modifications include one or more of:determination of one or more accounts, associated with the first entity,to open, determination of one or more accounts, associated with thefirst entity, to close, or modification of one or more accounts,associated with the first entity, to form a joint account with a secondentity.
 7. The method of claim 1, wherein determining the sets ofrelated entities is based on one or more socioeconomic factors.
 8. Adevice, comprising: one or more memories; and one or more processors,coupled to the one or more memories, configured to: perform a trainingoperation to generate a first model by portioning transactioninformation, entity information, information identifying an event, atheme, or a transaction parameter associated with a plurality of typesof relationships into a training set, a validation set, and a test set;determine, using the first model, sets of related entities associatedwith a first entity and a second entity, wherein the sets of relatedentities are associated with sets of accounts; identify, using a secondmodel, a type of account associated with each account included in thesets of accounts associated with the sets of related entities;determine, using the second model, a commonality factor associated withthe type of account; determine, using the second model, to include thetype of account in a recommended set of accounts based on thecommonality factor; determine, using the second model and based on therecommended set of accounts, information identifying one or moremodifications associated with the first entity; and perform, based onthe one or more modifications, one or more actions associated with thefirst entity and the second entity.
 9. The device of claim 8, whereinthe one or more processors, to perform the one or more actions, areconfigured to one or more of: provide a recommendation to form anaccount associated with the type of account, or open the account,associated with the type of account, in association with the firstentity and the second entity.
 10. The device of claim 8, wherein thetype of account includes one or more of: a savings account, a checkingaccount, or a brokerage account.
 11. The device of claim 8, wherein theone or more processors, to determine to include the type of account inthe recommended set of accounts, are configured to: compare thecommonality factor against a threshold commonality factor to determineto include the type of account in the recommended set of accounts. 12.The device of claim 8, wherein the commonality factor is associated witha quantity or percentage of the sets of accounts having the type ofaccount.
 13. The device of claim 8, wherein the one or moremodifications include one or more of: determination of one or moreaccounts, associated with the first entity, to open, determination ofone or more accounts, associated with the first entity, to close,modification of one or more accounts, associated with the first entity,to form a joint account with the second entity, determination of one ormore accounts, associated with the second entity, to open, determinationof one or more accounts, associated with the second entity, to close, ormodification of one or more accounts, associated with the second entity,to form a joint account with the first entity.
 14. The device of claim8, wherein determining the sets of related entities is based on one ormore socioeconomic factors.
 15. A non-transitory computer-readablemedium storing a set of instructions, the set of instructionscomprising: one or more instructions that, when executed by one or moreprocessors of a device, cause the device to: perform a trainingoperation to generate a first model by portioning transactioninformation, entity information, information identifying an event, atheme, or a transaction parameter associated with a plurality of typesof relationships into a training set, a validation set, and a test set;determine, using the first model, sets of related entities associatedwith a first entity or a second entity, wherein the sets of relatedentities are associated with sets of accounts; identify, using a secondmodel, a type of account associated with each account included in thesets of accounts associated with the sets of related entities;determine, using the second model, a commonality factor associated withthe type of account; determine, using the second model, to include thetype of account in a recommended set of accounts based on thecommonality factor; determine, using the second model and based on therecommended set of accounts, information identifying one or moremodifications associated with the first entity; and perform, based onthe one or more modifications, one or more actions associated with thefirst entity or the second entity.
 16. The non-transitorycomputer-readable medium of claim 15, wherein the one or moreinstructions, that cause the device to perform the one or more actions,cause the device to: provide a recommendation to form an accountassociated with the type of account, and open the account, associatedwith the type of account, in association with the first entity.
 17. Thenon-transitory computer-readable medium of claim 15, wherein the type ofaccount includes one or more of: a savings account, a checking account,or a brokerage account.
 18. The non-transitory computer-readable mediumof claim 15, wherein the one or more instructions, that cause the deviceto determine the type of account in the recommended set of accounts,cause the device to: compare the commonality factor against a thresholdcommonality factor to determine to include the type of account in therecommended set of accounts.
 19. The non-transitory computer-readablemedium of claim 15, wherein the commonality factor is associated with aquantity or percentage of the sets of accounts having the type ofaccount.
 20. The non-transitory computer-readable medium of claim 15,wherein the one or more modifications include one or more of:determination of one or more accounts, associated with the first entity,to open, determination of one or more accounts, associated with thefirst entity, to close, modification of one or more accounts, associatedwith the first entity, to form a joint account with the second entity,determination of one or more accounts, associated with the secondentity, to open, determination of one or more accounts, associated withthe second entity, to close, or modification of one or more accounts,associated with the second entity, to form a joint account with thefirst entity.